Paper details: Please answer ONLY the following Five question tax issues with analysis based on PRIMARY AUTHORITY see attached SAMPLE (and site the citation the same way is in the attached sample ( Example Rev. Rul. 84-101, 1984-28 I.R.B. 5.) 1-What is the character of any taxable gain generated by the sale of Eli Wolford interest to Kevin Dole? 2-What is the amount of any taxable gain generated by the sale of Eli Wolford interest to Kevin Dole? 3-Is there a tax consequence to the existing partners due to the transfer of interest in the partnership by Eli Wolford? 4-Will the company partnership structure be affected by the sale of interest by Eli Wolford? 5-ADD your own good tax issue

The aim of this workshop is to give you some experience using standard statistical treatments of data using statistical software. The package we will use for this workshop is Minitab. It is the package used as the standard statistical software by the Mathematics and Statistics courses at RMIT and Chemistry also has a site licence for it. Most of the analyses in this workshop can be done using Excel but Excel is not very user-friendly for statistical analyses. The analyses in Minitab can be done from simple pull-down menus and there is a good on-line help facility. You can copy-and-paste data from Excel into Minitab. For the assessment you should enter your results into the attached pro forma.

Before you attempt the case studies in this assignment you should try the worked examples in modules 1-4

Case Study 1

Analyst A

Analyst B

6.42

6.40

6.41

6.54

6.43

6.52

6.38

6.58

The above results were obtained by two analysts using a new method for determination of Nickel in a standard reference alloy containing a certified value of 6.49% Ni

For this data we want to determine the standard statistics:- (i) mean (ii) variance (iii) standard deviation and (iv) confidence intervals for the mean. This data enables us to answer the following questions:-

Which analysis is the most accurate? (i.e. closest to the certified value)

Which analysis is the most precise? (i.e. which has the smallest spread, or variability, of values

As well we can use the t-test to answer the following:-

Is there any evidence, with either analyst, of a systematic error? I.e. does either average differ significantly from the certified value?

Do the results of each analyst differ significantly?

Analysis: Basic Statistics

Open up Minitab by clicking on the Minitab icon on your desktop

When you open the program you will notice it is divided into two areas – the data area (lower screen) and the output area. Enter data from the above table in columns C1 and C2.

Warning: make sure you start entering data in row 1 NOT in the cell immediately below the column heading (C1 etc). This cell is reserved for column labels (you may put a label here like ‘Analyst A’). Also make sure you don’t enter a column label in row 1. The whole column will then be formatted as text (C1-T) and cannot be used for analysis. If this happens delete the whole column and start again (clicking on ‘C1’ will highlight the whole column).

To get descriptive statistics click on Stat => Basic Statistics => Display Descriptive Statistics to get the basic statistics dialog box. Highlight C1 and C2 on the left and then click ‘Select’. Alternatively you can click in the Variable box and type C1 C2 . Then click OK and the output will appear in the output window. From the output data enter the values in the pro forma. Note that the output does not give the variance but you should be able to calculate it from the standard deviation.

Confidence Intervals

The confidence intervals for the mean can be obtained as follows: Stat => Basic Statistics => 1-sample t. Click on ‘confidence interval’ and leave at the default 95%

The confidence interval is of the form (low value, high value). To express ie interval in the form of ‘mean +/- deviation’ calculate the deviation as 0.5*(high – low)

Hypothesis Testing

We now want to test whether either sample deviates significantly from the expected (certified) value of 6.49. We need to formulate the null hypothesis (H_{o}). In all statistical testing the probability is then calculated of the null hypothesis being true. If there is a low probability (usually < 5% or p = 0.05) of H_{o} being true we reject it and accept the alternative (H_{1}). The null hypothesis generally considers any deviations as being just due to chance/ experimental error. In question (c ) we are looking a null hypothesis of the analytical result not being significantly different from the certified value i.e the mean value is actually 6.49. In question (d) our null hypothesis is that the two means are equal.

For question (c ) we apply a t test: Stat => Basic Statistics => 1-sample t as above but this time check the Test mean box and enter ‘6.49’ in this box. Remember in your conclusions that a result is significant (i.e reject H_{o}) if p < 0.05 (less than a 5% chance that H_{o} is ture i.e no significant difference in the mean value from the certified value).

For question (d) we also apply a t test, to compare two means: Stat = Basic Statistics => 2-sample t. Click on ‘Samples in different columns’ Click ‘First’ box and then double click on C1 in the variables column and similarly for C2 as ‘Second’. Accept ‘Not equal’ and ‘95’ as default values. The 95% confidence level given in the output is for the difference between the two means. The probability that this difference is actually zero (or not significantly different from zero) is given at the end of the output.

Case Study 2

A (mL)

B (mL)

C (mL)

D (mL)

14.03

13.98

14.13

14.16

14.09

13.90

14.23

14.23

14.07

13.79

14.08

14.10

Four students were asked to perform three triplicate titrations using the same titrimetric procedures. Test to see if the students’ results differ significantly

One-Way Analysis of Variance

Enter the above data in columns C3-C6 (with appropriate headings for the columns). We can then test for differences in the means of each column using 1-way ANOVA as follows:- Stat => ANOVA => 1-way (unstacked). Highlight all the columns C3-C6 in the left box and click ‘select’ . They should now appear in the response box. Click OK.

The output should be a typical ANOVA table (see the notes :measurement and assessment of variability.pdf for a full explanation of the ANOVA table). The key value is again the p value (p that H_{o} is true). We are testing here whether all four students are the same i.e their results do not differ significantly. We have to be careful about the alternative (H_{1}) hypothesis if we reject H_{o}. H_{1} is notthat the students are all different (why?). Minitab gives a diagram which can help in interpreting the results, showing each mean and confidence interval. Two results differ significantly if their CI’s don’t overlap. Note, however, that Minitab uses a pooled CI so they are all the same size. The diagram is thus just an indication but is still quite useful.

Case Study 3

Phosphorus (mg/kg)

Temperature (^{o}C)

Soil 1

Soil 1

Soil 2

Soil 2

230

18.2

18.4

18.2

18.5

260

18.6

18.9

18.4

18.1

290

17.7

18

18.1

17.8

320

17.1

17.4

17.8

17.5

An experiment was carried out on the determination of phosphorus in soils to examine the effect of temperature on the analysis. As a result of time and cost considerations it was only possible to carry out 16 experiments. There was insufficient soil for all 16, so two batches of soil were used. A randomised block design was used, giving these results. Test to see if the temperature affects the analysis, and if there is any difference between the soils. Is there any evidence of interaction between soils and temperature?

Two Way Analysis of Variance

This differs from the previous study in that there are two variables – temperature and soil. The data needs to be set out differently, as follows:-

In one column enter all 16 phosphorus analytical values (18.2, 18.4 ….17.5)

You also need two coding columns. Make one column the code for temperature and give a code (1 – 4) for each temperature.

Enter in a third column the code (1-2) for the soil type. Thus the first value (18.5) will have 18.5, 1, 1 in the three columns while the last value (17.5) would have 17.5,4,2 (i.e 320^{o}C and soil 2)

Carry out the two way ANOVA:- Stat=> ANOVA => 2-Way. In the response field enter the column for phosphorus and enter the other two variables in the row and column boxes. Check the ‘display means’ boxes.

Because there are two variables there are now null hypotheses for each variable (e.g no significant difference between soils i.e mean [P] for soil 1 = mean [P] for soil 2). As with all our previous testing the p value is the probability that this is true and we reject H_{o} if p is low ( < 0.05) and hence conclude there is a significant difference.

In 2 way ANOVA the possibility of variable interaction is also tested. An interaction means, for example, that temperature differences depend on soil type. If we see temperature differences with soil 1 but not soil 2 this would be an interaction effect. Again the diagram of means and CIs can be an indication of where differences occur.

Case Study 4

X(^{o}C)

20

21

22

23

24

25

26

27

28

29

30

Y(%)

10

15

14

17

18

19

20

23

24

23

28

A study was made on the effect of temperature on the yield of a chemical process. The results are shown in the above table. Carry out a regression analysis of the data. Predict the % yield when the temperature is 22.5^{o}C

Linear Regression

The analysis can be carried out as follows:-

Stat => Regression => Regression. Enter the Y column in the response box and the X column in the predictors box. Click on options and in the ‘prediction intervals for new responses’ enter ‘22.5’ (note if you have more than one X for prediction you can enter them in a new column and put the column in this box).

The output gives you the model (the regression equation), values of the intercept (constant) and gradient (predictor) with statistical information on these parameters. A full ANOVA table is also shown . For full interpretation of this output you should consult the ‘Chemometrics2: Regression” notes.

The t tests determine whether the gradient or the intercept are significantly non-zero. 9Again, check the p values)

The confidence intervals for the gradient and intercept can be determined as +/- s_{a}*t_{n-2,.05} and similarly for s_{b} . s_{a } and s_{b} are the standard deviations of gradient and intercept respectively. T is the critical t value for n-2 (n = number of pairs of data) degrees of freedom and 0.05 significance level. This value can be obtained from t tables.

At the end of the output is the predicted Y when X = 22.5, along with the confidence (CI) and prediction (PI) intervals. The full meaning of these terms is explained in the regression notes.

Case Study 5

X concentration (mg/L)

40

50

60

70

80

90

40

60

50

80

Y colorimeter reading

69

175

272

335

409

415

72

265

180

412

The following data indicates the relationship between the amount of b- erylthriodine in an aqueous solution and the colorimeter reading of turbidity. Carry out a regression analysis as above. Assess whether a linear model is appropriate for this data.

Carry out linear regression: Stat => regression =>regression. Enter the columns for the X and Y data in the predictor and response boxes.

To investigate whether a linear model is appropriate (i.e . a straight line fits the data better than a polynomial, exponential or logarithmic curve, or some other model) we carry out a ‘lack-of-fit’ test. Minitab has two of these. The first is the ‘pure error’ test, which is standard for testing ‘lack-of-fit’ but requires that at least some of the X values be replicated. The second test is non-standard but does give an indication even when there are no replicates. To use this test click on ‘Options’ in the regression screen and click ‘pure error’ and ‘data subsetting’. The null hypothesis is that there is no curvature (i.e. a linear model is adequate) so the p value reflects the extent to which this is true.

We can also examine the graph of the data and graphs of residuals. To get a regression plot of the fitted line select: Stat => regression =fitted line plot. Enter the columns for X and Y in the predictors and response boxes as above. Click on ‘options’ and check ‘display confidence bands’ and ‘display prediction bands’

To display plots of the residuals , in the regression screen click on ‘graphs’ and clock on the ‘normal plot of residuals’ and ‘residuals vs fits’ boxes.

Examining graphs and plots of residuals can help determine whether there are any outliers. While a curve may be the best fit to all the data , one outlier can greatly affect this. Residuals should be randomly scattered around the X axis of the residuals-X plot, and the normal plot of the residuals should be linear (see Regression notes for further discussion).

Exercise: ‘The Inverse Calibration Problem’

An unknown erlythroidine solution gives a colorimeter reading of 402. What is the predicted concentration? What are the confidence limits for this prediction if (i) this was a single measurement (ii) it was an average of several measurements.

This question is typical of the sort of problem frequently encountered in analytical calibrations. We cannot proceed as in case study 4 because we now wish to determine X from a known Y (the ‘inverse’ problem). Least squares analysis assumes the error is in the Y determinations. However the error in X determined from Y can be estimated from the standard deviation of the interpolated X_{0}:

S_{X0} = }^{0.5}

A spreadsheet has been set up to carry out this calculation. It can be found in s:\hons\data treatment\invcalib.xls, or on the data treatment site on the DLS.

Carry out the determination of the prediction and confidence intervals as follows:-

(i) Copy the (X,Y) calibration values for Q.5 from Minitab . Paste them in the invcalib spreadsheet (inverse calibration sheet) in the X and Y columns at the left.

(ii) enter ‘402’ in the y_{o} cell (highlighted in green) and ‘1’ in the highlighted cell for ‘m’. This then gives the predicted value for x and the CI if 402 is a single measurement

(iii) change the ‘m’ values to 5. See what effect it has on the CI .

Part B You are to carry out an evaluation of your project , in terms of the data collection and treatment aspects of the project. If you do not have a project yet, think of a ‘hypothetical’ project in your discipline area which might be carried out. This is to be presented as a brief summary , set out as follows.

Project Overview

Give the project title (including supervisor). State the aims of the project – what do you want to achieve? Why is the study being carried out?

Define the response(s)

What is being measured? List your types of responses. Are these responses qualitative or quantitative? If qualitative can they be turned into quantitative responses (e.g by giving a score or rating). Are they discrete or continuous?

Define the Factors

What factors (variables) affect your results (responses)?

Rank the factors – known to influence, suspect to influence, unknown effect

Divide the factors into controllable and uncontrollable

Identify sources of error

What are the sources of error in your study? How can they be minimised? You need to consider the effect of sampling – usually you cannot test the whole population so you want to take a sample of the population. How do you select the sample? How big should the sample be?

Name………………………… Student Number ………………………

Case Study 1

Basic Statistics

Analyst A

Analyst B

Mean

Standard Deviation

Variance

Confidence Interval (95%)

Which analyst is the most precise? ……………….. Reason?…………………..

Which Analyst is the most accurate?………………. Reason? …………………

Test

Null Hypothesis

p

Significant?

A differs from standard?

B differs from standard?

A and B differ from each other?

Case Study 2

H_{0} …………………………………… H_{1} ………………………………………..

p ……………… significant? ……………………………………

Does the diagram indicate anything further about the students? …………………………….

Case Study 3:

F

p

Significant?

Temperatures

Soils

Interactions

Case Study 4

Predicted equation (model)

P for hypothesis (b= 0)

Significant? i.e. is the gradient non-zero?

Standard deviation of slope (s_{b})

t (from tables)

Confidence intervals for b_{1}(+/- ts_{b})

Predicted yield for 22.5^{o}C

Confidence interval

Prediction interval

Case Study 5

Model (equation) ……………………………………………….

Lack-of-fit significant? …………………………………………

Check the plot of the data and the residuals. Is there any evidence of an outlier? What further treatment of the data would you suggest?

A psychologist conducted a study to examine the nature of the relation, if any, between an employee’s emotional stability (X) and the employee’s ability to perform in a task group (Y). Emotional stability was measured by a written test for which the higher the score, the greater is the emotional stability. Ability to perform in a task (Y=1 if able, Y=0 if unable) was evaluated by the supervisor. The psychologist believes a logistic regression model is appropriate for studying this suspected relation. The dataset for this exercise is “emoc.accdb” (Access format) and can be download from e leaning.

Conduct an appropriate analysis, a complete write-up is required, supplement with tables and figures as necessary; if possible include a scatter plot with the fitted logistic response function.

Use the dataset from exercise 10 and conduct a power analysis for a one-way ANOVA and 2B- factorial ANOVA. Write up your findings.

Logistic

Use the dataset from exercise 9 and conduct a power analysis for the logistic regression. Write up your findings.

Regression

Provided below are test data for three groups of students taught by Mr. Smith, Ms. Jones, and Ms. Green along with student pretest data. Use test as the dependent variable and the teacher and pretest as the independents to fit a multiple regression model testing your residual analysis. Then conduct a power analysis. Write up your findings.

Test Teacher Pretest

38 Smith 21

39 Smith 26

36 Smith 22

45 Smith 28

33 Smith 19

43 Green 34

38 Green 26

38 Green 29

27 Green 18

34 Green 25

24 Jones 23

32 Jones 29

31 Jones 30

21 Jones 16

28 Jones 29

Include in your write-up analysis a discussion of outliers and influential data points, residual analysis (are they normal), relevant tables and plots and a complete interpretation of the parameter estimates and overall model.

Power Analysis Extra Credit (1 pt)

The EC is due with Exercise 6; please use a separate attachment for it in the eLearing drop box.

Write a paragraph to a page review (APA format with a complete APA citation) of a primary source using power analysis. Be sure to answer the following questions in your summary: (a) What is the general problem under study, (b) specifically what research question/hypothesis are the researchers testing within the specified analysis (i.e., what groups are being compared), (c) what are the covariate variable(s), (d) what is the criterion variables, (e) did the authors conducted residual analysis, (f) is the analysis related to the general problem the researchers are investigating (g) describe the power analysis and (h) what were the findings of the specific analysis?

Use the data below and conduct a 2-B factorial ANOVA, complete with an interaction analysis if necessary. The data describe performance on statistics project in a sample of 28 graduate students. A complete write-up is required, supplement with tables and figures as necessary. Subjects were classified as master or doctoral and students either self-selected a self-study curriculum, a laboratory curriculum or a traditional lecture curriculum.

SAS input

data Ex9;

input id

group

study

score @@;

datalines;

01 1 1 34 02 1 1 33 03 1 1 28 04 1 1 29 05 1 1 33

06 1 2 34 07 1 2 31 08 1 2 28 09 1 2 31 10 1 2 .

11 1 3 45 12 1 3 29 13 1 3 38 14 1 3 34 15 1 3 33

16 2 1 30 17 2 1 31 18 2 1 39 19 2 1 30 20 2 1 34

21 2 2 35 22 2 2 36 23 2 2 37 24 2 2 41 25 2 2 39

26 2 3 . 27 2 3 28 28 2 3 41 29 2 3 47 30 2 3 45

;

run;

proc format;

valuegfmt 1=’Master’

2=’Doctoral’;

valuesfmt 1=’SelfStudy’

2=’LAB’

3=’Lecture’;

run;

SPSS input

data list list

/id group study score.

begin data.

01 1 1 34

02 1 1 33

03 1 1 28

04 1 1 29

05 1 1 33

06 1 2 34

07 1 2 31

08 1 2 28

09 1 2 31

10 1 2 .

11 1 3 45

12 1 3 29

13 1 3 38

14 1 3 34

15 1 3 33

16 2 1 30

17 2 1 31

18 2 1 39

19 2 1 30

20 2 1 34

21 2 2 35

22 2 2 36

23 2 2 37

24 2 2 41

25 2 2 39

26 2 3 .

27 2 3 28

28 2 3 41

29 2 3 47

30 2 3 45

end data.

val lab group 1 “Master” 2 ‘”Doctoral’.

val lab study 1 “SelfStudy” 2 “LAB” 3 “Lecture”.

Less than Full Rank Design Extra Credit (1 pt)

The EC is due with Exercise 9; please use a separate attachment for it in the eLearing drop box.

Write a paragraph to a page review (APA format with a complete APA citation) of a primary source of an unbalanced study. Be sure to answer the following questions in your summary: (a) What is the general problem under study, (b) specifically what research question/hypothesis are the researchers testing within the specified analysis (i.e., what groups are being compared), (c) what was the statistical approach taken to accommodate the nonorthogonality, (d) what is the outcome variable, (e) did the authors present a test of any of the linear model assumptions, (f) is the analysis related to the general problem the researchers are investigating and (g) what were the findings of the specific analysis?

For answers contact us via proessaywiters@gmail.com

Math 203 Course Project You may only use quantitative data for this project. The final project should be written in paragraph form, but should include all the information listed below. Obviously, it will contain graphs and charts, but do not present the information as individual numbered problems. Think of it as if you were writing an article to go into a newspaper or magazine.

First, decide on a random variable that you are interested in knowing more about. You will be gathering data for this variable. You may do your own survey to get the data (use sampling techniques described in chapter 1) or you may use data found online. Your sample size should be no smaller than 10 and no larger than 50. Define your population and describe how you got your sample data set. lnclude the raw data in your report.

ls your data discrete or continuous? ls your data nominal, ordinal, interval or ratio?

Construct a frequency distribution for your data set using 5 to 8 classes (you choose how many). What is your class width? Your frequency distribution should include class limits, class boundaries, class midpoints, frequency, cumulative frequency, and relative frequency.

Construct a histogram, frequency polygon and ogive for your data seU labeling each graph appropriately. Describe the shape of your data (i.e. ls it symmetrical, skewed right or skewed left?).

Calculate the measures of central tendency for your sample (mean, median, mode and midrange).

Calculate the measures of variation for your sample (range, variance and standard deviation). 7 . Use Chebyshev’s Theorem to find the range in which at least 75% of the data fall.

ldentify the five-number summary and find the interquartile range. Construct a boxplot of your data set. Does your data contain any outliers (identify specific criteria used to determine)?

Construct two probability questions about your data and solve each. Here, you are creating the problenn and then solving. Examples below: a. For discrete data: P(exactly X) ; P(at least X); P(at most X); P(less than X); P(more than X) b. For continuous data: P(X between 2 values); P(more than X); P(less than X) l-0. Constructa90% confidence intervalforthe population parameterfromyourdata (includethecriticalvalue that would be used).

Construct a hypothesis test using your data. Here, you will be making a guess about the population parameter. Show both the traditional method (give the criticalvalue) and p-value method. Use a level of significance of 0.05. NOTE: The final project must be neat, organized and easy to read. I will NOT accept nor grade any rough drafts or scratch work!l! lf you are familiar with computer software like Excel, you may use that for the graphs, otherwise graphs may be donebyhand.

Q1.Design an iterative algorithm to multiply odd integers. For example, if a given n is an odd integer, then the product is 1 * 3 * 5 * 7 * … * n; if n is an even integer, then the product is 1 * 3 * 5 * 7 * … * (n-1).

Q2.mplement your above iterative algorithm in Java with the following method header.

public static int oddProduct(int n)

Selected Answer:

public class Main

{

public static int oddProduct(int n)

{

int i = 1;

int product = 1;

if(i%2==0)

{

while(i<(n-1))

{

product*=i;

i+=2;

}

}

else{

while(i<(n))

{

product*=i;

i+=2;

}

}

}

public static void main(String[] args)

{

int product;

int n = 9;

product= oddProduct(n);

System.out.println(product);

}

}

Response Feedback:

off by one

Q3.Design a recursive algorithm to multiply odd integers as specified in the previous question (Q1).

Q4.

Implement your above recursive algorithm in Java with the following method header:

public static int oddProduct(int n)

Selected Answer:

public class Main

{

public static int oddProduct(int n)

{

if (n == 1)

return 1;

else

return n*oddProduct(n-2);

}

public static void main(String[] args)

{

int product;

int n = 9;

product= oddProduct(n);

System.out.println(product);

}

}

Response Feedback:

What if n is even?

Q16.

Solve the recurrence relation using master’s theorem: T(n) = 9T(n/3) + n. Show all steps.

Q17.

Solve the recurrence relation: T(n) = 2T(n/2) + n, T(1) = 1, using the substitution method. Your answer must contain the following 3 parts. 1) detailed steps leading to the closed form; 2) the exact closed formula; 3) complexity in big O notation. Write an answer without steps will receive zero point. You can either use the Math editor in Blackboard, or write on a separate paper (don’t forget your name).

Q19.

You just realized that homework for CPS 340 is due in just 8 hours, yet you have not started. There are four questions in the homework, and each question with different points require certain number of hours to complete (see below). Use a brute-force strategy to maximize your points for the homework.

For your Problem:

hours left – 8

hours -[1,5,3,4]

points -[15,10,9,5]

Hours Left ->

0 1 2 3 4 5 6 7 8 -> hours left

0

0

0

0

0

0

0

0

0

0

15

15

15

15

15

15

15

15

0

15

15

15

15

15

25

25

25

0

15

15

15

24

24

25

25

25

0

15

15

15

24

24

25

25

29

so for [1,3,4] hour combination you can get the best points 29. questions -> [1,3,4]

For answers Contact us via proessaywiters@gmail.com

Identify and describe the function of the main parts of the microscope.

Describe the proper use and care for the microscope.

Describe the relationship between magnification, field of view, and resolution.

Draw and label cellular structures observed under the microscope.

Compare the size and identify key structures of various cell types, including prokaryotic and eukaryotic cells.

Background:

Microscopes are designed to make objects visible that are too difficult or too small to see with the human eye. All types of scientists use microscopes. Different kinds of microscopes are designed to visualize various magnifications and types of specimens.

This week’s lab will introduce you to the compound microscope, and give you an opportunity to use this type of microscope to observe various cell types up close (we’ll use a virtual one!). As we are also learning about cell structure, be sure to look to your notes to help you identify organelles and other cell structures, when applicable.

There are 3 parts to today’s lab: Part 1 introduces you to the parts and associated functions of the microscope, Part 2 gets you using the virtual microscope by looking at slides, and Part 3 has you looking at mitotically dividing cells under the virtual microscope.

In Part 1 of today’s lab, the goal is to familiarize yourself with the parts of the microscope and how to care for the microscope.

Click on the “Guide” button along the bottom of the screen. Read and click through the ‘chapters’ from the Introduction to Microscope Care. All of the ‘chapters’ except the Introduction are made up of several pages that you need to click through (e.g., the Overview page has 12 short pages for you to read and click through). Take notes and answer the numbered questions below.You do not need to upload your notes for this assignment.

Define the function for each of the following parts of the microscope:

Arm, Base, & Body of the Microscope

Coarse Focus Knob

Fine Focus Knob

Diaphragm

Eyepiece

Objectives

Immersion Oil Lens

Immersion Oil

Lens Paper/Tissue

Nosepiece

On/Off Switch

Stage

Condenser Lens

Define the following terms:

field of view

micrometer

paracentered

parfocal

resolution

When you are finished, click the “Close” button at the top, right of the reference guide.

Click on the “Learn” button along the bottom of the screen. NOTE: There are 4 pages to this interactive click-through. Be sure to complete and answer all the questions below before moving on to the next page as you will NOT be able to go back.

The microscope presented on the first screen is very similar to the microscopes we have in our labs at CCSF. You will need to click through all the 16 different parts before moving on to the next part of this “Learn” module. As you work through these microscope parts, be sure to complete the following before moving on:

Compare your notes from the previous section with what you read about here. Include any information that you might have missed from the last section. You do not need to upload your notes for this assignment. Answer the numbered questions below.

Define the function for each of the following items:

Stage Adjustment Knob

KimWipes

Slides

Identify where the parts of the microscope are located (click on them to see). List the parts of the microscope where the path of light goes through, from the illuminator (located right under the stage) to when it reaches your eye, in order.

You will next go through the various lenses of the microscope. The eyepiece, scanning (4x, red), low power (10x, yellow), high power (40x, blue), and oil immersion (100x, white) lenses all have a nosepiece thread, color band, and magnification listed on the objective. As you click through each of these lenses, answer the following numbered questions:

What is a useful feature of each lens?:

eyepiece

scanning

low power

high power

immersion oil

Click “Main” at the bottom left of the lens window to return to the main page. Then click “Explore” to move to the next section.

You will next see how the differentlenses can magnify specimens. Click the ? to open the slide box and then click on Animal Slides and Spider Leg to view that slide. Bring the specimen into focus at 4X using the course focus, fine focus and light adjustment. Then, answer the numbered questions below.

Draw the specimen at 4X.

Select the 10X objective, and make the necessary adjustments to bring the specimen into focus.

Draw your specimen at 10X.

Select the 40X objective, and make the necessary adjustments to bring the specimen into focus.

Draw your specimen at 40X.

Select the 100X objective, and make the necessary adjustments to bring the specimen into focus.

What additional steps did you need to take to use the 100X objective? (HINT: The tools you need are indicated by a ? mark in the image.)

Draw your specimen at 100X.

Did you try using the coarse adjustment while looking through the 100X objective? What happened? If you didn’t try it, try it now and record what happens.

Put your slide away and clean the microscope.

Next, let’s calculate the magnifications we just viewed.

At CCSF, our eyepieces are 10x. If we were looking through our 4x, 10x, 40x, and 100x objectives, what would the total magnification be for each?

The equation is total magnification = objective magnification x eyepiece magnification

4X total magnification

10X total magnification

40X total magnification

100X total magnification

Click “Main” at the bottom left of the lens window to return to the main page.

OPTIONAL: Click “Test” to test your understanding of what you’ve learned so far.

Part 2: Observing Specimens Under the Microscope

You should now have a good understanding of the various microscope parts and their functions, as well as how to care for the microscope. In Part 2 of today’s lab, we will learn how to use the microscope.

Generally, here are the steps of using a “real” microscope:

Getting started

Plug in and turn on light source (lamp);

Clip slide you want to observe on the stage and center it over the light;

Raise the stage with the coarse focus as far as it can go.

Initial focus

ALWAYS start with the short scanning lens (4x objective);

Turn the coarse focus down until the image is clear (I suggest even turning it even a bit further than this and then back again to find the sharpest image possible);

Re-center the slide and adjust the light (including the diaphragm) if needed.

Higher magnification

Watching from the side, rotate to the low power lens (10x objective) SLOWLY (so lens DOES NOT hit the slide – if it looks like it will hit, then STOP and start over from the steps under initial focus);

Sharpen image with the fine focus; the coarse focus may not be necessary;

Re-center slide and adjust the light if needed;

Repeat for high power lens (40x objective). The coarse focus should not be used at 40X or above.

Oli immersion lens

If a higher magnification is necessary, rotate the oil immersion lens almost into the place. BEFORE clicking the oil immersion lens into place, however, place a drop of oil directly on top of the center of your slide;

SLOWLY slide your oil immersion lens into place, making sure that the lens does not hit the slide as before. Once in place, the lens should be in the oil itself;

Sharpen image with the fine focusonly;

Once done, be sure to clean the slide and lens with lens paper.

Ok, are you ready? Let’s finally look at some slides!

Click on the “Explore” button along the bottom of the screen. Click to open the slide box and notice all the choices of specimens that you can look at. As you observe specimens today, follow these general guidelines:

Label your drawings. Whenever you make a drawing from what you see in a microscope, always include the name of the specimen, label any visible structures, and include the total magnification.

Take note of the size of your drawing. Your drawings should always be about ~7cm (almost 3 inches) in length and in width so that you can provide enough detail in what you are observing. Use the provided Microscope & Cells Lab Assignment sheet to guide you on your drawings if needed.

Find the sharpest image possible. Always try to find the clearest image using the coarse focus first, and then the fine focus.

Always start at the 4x objective. This compound microscope is parfocal. So, always remember to focus as well as you can at the 4x objective first. If you do this correctly, as you move higher in magnification, all you will need to do is to toggle the fine focus and light. Never use the coarse focus on high magnification and never skip an objective (don’t jump from 4X to 40X, for example).

There are 5 specimens that I want you to draw today (listed below). Draw the 5 specimens at the magnification indicated and answer the related questions. Remember to label your drawings as indicated above (specimen name, label structures, total magnification).

The letter “e” (in the ‘Sample Slides’ deck) 10x. As you increase in magnification, can you see less or more of the letter “e”?

Plant cells (in the ‘Plant Slides’ deck) at 40x. Be sure to label the nucleus, chloroplasts, cytoplasm, cell wall, and cell membrane.

Stratified Squamous Epithelium (in the ‘Human Slides’ deck) at 40x. Be sure to label the visible cell structures here. How are these cells similar and different from the plant cells?

Gram Stain Mix (in the Bacteria Slides’ deck) at 40x AND 100x (2 drawings). Label the rod-shaped bacteria (Bacillus subtilis) from the cocci (round)-shaped bacteria (Neisseria subflava). At which magnification is it easier to distinguish the bacteria’s shape? How are these cells different from the eukaryotic cells you just observed?

One more additional slide of your choice in the ‘Human Slides’ deck. Look through the different slides that are available to you to look at. Pick one to view and draw – be sure to use an appropriate magnification and label any structures that you can identify. How are these cells different from the squamous epithelium cells you observed above?

Part 3: Observing Mitosis Under the Microscope

Mitosis is the process in which somatic (non-sex) cells divide. Since the main purpose of mitosis is for growth and repair, the resulting cells that are produced (known as daughter cells) through mitosis are exactly the same as the original (parent) cell. Think about it: If your body needs to repair and replace the skin cells on your finger after a papercut, mitosis will enable your body to produce the same type of skin cells to maintain its original function.

Similarly, mitosis occurs in other organisms besides us. In plants, a common place where we would see mitotic growth is at the onion root tip: Cells are constantly dividing in this region as the root elongates and moves deeper into the soil. In Part 3 of today’s lab, you will observe the various stages of mitosis at an onion root tip. Please have your book and notes on mitosis to help you identify the various stages under the microscope.

After clicking on the “Explore” button of the Virtual Microscope, click on the box of slides and go into the ‘Plant Slides’ deck. From here, click on the Onion Root Tip slide and use the microscope to focus up to the 40x objective (starting with 4X, of course!). After scanning this field of view, complete the following:

Draw a representative onion root tip cell in interphase. Repeat this for a cell in prophase, metaphase, and anaphase. You should have a total of 4 cell drawings. For each drawing, label any structures you can identify (nuclear membrane, chromosomes, spindle fibers, cell wall). Write down a brief description of what is happening in each of your cells above (e.g., the chromosomes are aligned along the center of the cell). Don’t forget to label each of your drawings with a specimen name, label structures, and total magnification.

interphase

prophase

metaphase

anaphase

Next, look at the Whitefish Metaphase slide (in the ‘Animal Slides’ deck) at 40x. Identify a cell(s) in interphase, prophase, metaphase, and anaphase? You can click and drag on the specimen to move it around in the field of view.

interphase

prophase

metaphase

anaphase

How are these dividing cells different and/or similar to the dividing cells in the onion root tip?

You were not able to see a clear image of telophase and cytokinesis in these slides. How is this stage of mitosis different in a plant cell versus an animal cell?

Below is another image of the onion root tip cell from a different microscope slide. Indicate that you can identify cells in interphase, prophase, metaphase, anaphase, and telophase by drawing an arrow to each with a label.

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BUSINESS DATA MINING (IDS 472) HOMEWORK 2 • You should submit a report in pdf or word in blackboard in addition to your R script file. • One submission is sufficient for the entire group. • Please include the names of all team-members in your write up and in the name of the file. • Please include any R codes you use to answer the questions in your pdf report. Problem 1. Explain what each of the following R functions do? You can run them in R and check the results. (a) c(1, 17, −6, 3) (b) seq(1, 5, by=0.5) (c) seq(0, 10, length=5) (d) rep(0, 5) (e) rep(1:3, 4) (f) rep(4:6, 1:3) (g) sample(1:3) (h) sample(1:5, size=3, replace=FALSE) (i) sample(c(2,5,3), size=4, replace=TRUE) (j) sample(1:2, size=10, prob=c(1,3), replace=TRUE) (k) c(1, 2, 3) + c(4, 5, 6) (l) max(1:10) (m) min(1:10) (n) range(1:10) (o) matrix(1:12, nr=3, nc=4) (q) Let a ← c(1,2,3), b ← c(10, 20, 30), c ←c(100, 200, 300), d ← c(1000, 2000, 3000). What does the function rbind(a, b, c, d) do? What does cbind(a, b, c, d) do? 1 2 HOMEWORK 2 DUE DATE: FRIDAY, SEPTEMBER 25 AT 11:59 PM (r) Let C be the following matrix a b c d 1 10 100 1000 2 20 200 2000 3 30 300 3000 What is sum(C)? What is apply(C, 1, sum)? What is apply(C, 2, sum)? (s) Let movies ← c(“SPYDERMAN”,“BATMAN”,“VERTIGO”,“CHINATOWN”). What does lapply(movies, tolower) do? Notice that “tolower” changes the string value of a matrix to lower case. (t) Let x ← factor(c(“alpha”, “beta”, “gamma”, “alpha”, “beta”)). What does the function levels(x) return? (u) c ← 35:50 (v) c(1, 2, 3) + c(4, 5, 6) c(1, 2, 3, 4) + c(10, 20) (x) sqrt(c(100, 225, 400)) Problem 2. Create the following vectors in R. a = (5, 10, 15, 20, …, 160) b = (87, 86, 85, …, 56) Use vector arithmetic to multiply these vectors and call the result d. Select subsets of d to identify the following. (a) What are the 19th, 20th, and 21st elements of d? (b) What are all of the elements of d which are less than 2000? (c) How many elements of d are greater than 6000? Problem 3. This exercise relates to the College data set, which can be found in the file College.csv. It contains a number of variables for 777 different universities and colleges in the US. The variables are • Private : Public/private indicator • Apps : Number of applications received • Accept : Number of applicants accepted • Enroll : Number of new students enrolled • Top10perc : New students from top 10% of high school class • Top25perc : New students from top 25% of high school class • F.Undergrad : Number of full-time undergraduates BUSINESS DATA MINING (IDS 472) 3 • P.Undergrad : Number of part-time undergraduates • Outstate : Out-of-state tuition • Room.Board : Room and board costs • Books : Estimated book costs • Personal : Estimated personal spending • PhD : Percent of faculty with Ph.D.’s • Terminal : Percent of faculty with terminal degree • S.F.Ratio : Student/faculty ratio • perc.alumni : Percent of alumni who donate • Expend : Instructional expenditure per student • Grad.Rate : Graduation rate (a) Read the data into R. Call the loaded data “college”. Explain how you do this. (b) How many variables are in this data set. What are their measurements? How do you get these information? (c) Use the function colnames() to change the “Top10perc” and “Top 25per” variables names to “Top10” and “Top25”. (d) Look at the data. You should notice that the first column is just the name of each university. We don’t really want R to treat this as data. However, it may be handy to have these names for later. Try the following commands:

rownames (college) → college [,1] You should see that there is now a row.names column with the name of each university recorded. This means that R has given each row a name corresponding to the appropriate university. R will not try to perform calculations on the row names. However, we still need to eliminate the first column in the data where the names are stored. Write a code to eliminate the first column. (e) Add a column to indicate the acceptance rate for each university (acceptance rate = number of accepted applications / number of applications received). (f) Provide a summary statistics for numerical variables in the data set. (g) Use the pairs() function to produce a scatterplot matrix of the first ten columns or variables of the data. Recall that you can reference the first ten columns of a matrix A using A[,1:10]. Can you observe any useful information in the plots? (h) Use the boxplot() function to produce side-by-side boxplots of Outstate versus Private. Do you observe any useful information in this plot? (i) Create a new qualitative variable, called Elite, by binning the Top10perc variable. We are going to divide universities into two groups based on whether or not the proportion of students coming from the top 10% of their high school classes exceeds 50%. Follow the code below. 4 HOMEWORK 2 DUE DATE: FRIDAY, SEPTEMBER 25 AT 11:59 PM Elite → rep (“No”,nrow(college)) Elite[college$Top10perc > 50] = “Yes” Elite = as.factor(Elite) college = data.frame(college,Elite) i. Explain each line of the above code. ii. Use the summary() function to see how many elite universities there are. Now use the plot() function to produce side-by-side boxplots of Outstate versus Elite. (j) Use the hist() function to produce some histograms with differing numbers of bins for a few of the quantitative variables. You may find the command par(mfrow=c(2,2)) useful: it will divide the print window into four regions so that four plots can be made simultaneously. Modifying the arguments to this function will divide the screen in other ways. (k) What is room and board costs of private schools on average ? (l) Create a new binary variable that is 1 if the student/faculty ratio is greater than 0.5 and 0 otherwise. (m) Compare the distribution of out of state tuition for private and public colleges. Problem 4. This exercise involves the “Auto” data set. (a) Remove the missing values from this data set. (b) What is the range of each quantitative predictor? You can answer this using the range() function. (c) What is the mean and standard deviation of each quantitative predictor? (d) Remove the 10th through 85th observations. What is the range, mean, and standard deviation of each predictor in the subset of the data that remains? (e) Using the full data set, investigate the predictors graphically, using scatterplots or other tools of your choice. Create some plots highlighting the relationships among the predictors. Comment on your findings. (f) Suppose that we wish to predict gas mileage (mpg) on the basis of the other variables. Do your plots suggest that any of the other variables might be useful in predicting mpg? Justify your answer. Problem 5. FiveThirtyEight, a data journalism site devoted to politics, sports, science, economics, and culture, recently published a series of articles on gun deaths in America. Gun violence in the United States is a significant political issue, and while reducing gun deaths is a noble goal, we must first understand the causes and patterns in gun violence in order to craft appropriate policies. As part of the project, FiveThirtyEight collected data from the Centers for Disease Control and Prevention, as well as BUSINESS DATA MINING (IDS 472) 5 other governmental agencies and non-profits, on all gun deaths in the United States from 2012-2014.You can find this dataset, called ”gun deaths.csv”, on blackboard. (a) Generate a data frame that summarizes the number of gun deaths per month. (b) Generate a bar chart with labels on the x-axis. That is, each month should be labeled “Jan”, “Feb”, “Mar” and etc. (c) Generate a bar chart that identifies the number of gun deaths associated with each type of intent cause of death. The bars should be sorted from highest to lowest values. (d) Generate a boxplot visualizing the age of gun death victims, by sex. Print the average age of female gun death victims. Answer the following questions. Generate appropriate figures/tables to support your conclusions. (e) How many white males with at least a high school education were killed by guns in 2012? (f) Which season of the year has the most gun deaths? Assume that – Winter = January – March – Spring = April – June – Summer = July – September – Fall = October – December – Hint: You need to convert a continuous variable into a categorical variable. (g) Are whites who are killed by guns more likely to die because of suicide or homicide? How does this compare to blacks and Hispanics? (h) Are police-involved gun deaths significantly different from other gun deaths? Assess the relationship between police involvement and other variables.