Statistics 101 Lab 2 LAVC Excel Project

Anonymous
timer Asked: Jan 31st, 2019
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Question Description

Need help with an excel project, One of my labs. I absolutely don't understand how to do this. I need every little detail very organized on my files. Please let me know you have any questions.

20190131030723lab_3_correlation_data.xls 1/31/2019 SCHOOL ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS ARLETA HS LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC FINAL 0 0 0 0 31 32 33 37 37 38 39 39 39 40 40 41 43 43 44 46 48 49 49 51 53 53 58 59 62 66 70 71 83 85 89 91 17 18 25 27 28 28 31 32 GRADE F F F F D C F C F C C D F C C C D D C B B A A C B B A A A D A C B B A B F F D F F F D F GPA 2 1.24 2.13 1.72 2.32 2.68 1.39 2.32 1.18 2.89 2.22 2 1.23 2.01 2.02 2.46 2.4 1.84 2.24 2.59 2.35 3.89 3.44 2.52 2.85 2.38 3.44 3.88 3.08 1.8 3.26 2.56 3.13 3.48 3.4 3.25 1.05 1.95 2.74 2.25 2.01 2.2 2.71 1.86 Randomly Selected LAVC Row #s 83 85 146 105 68 115 125 51 108 128 76 112 93 135 39 48 71 120 114 138 84 LAVC Monroe Randomly Selected Monroe Row #s 158 154 177 152 199 159 207 188 163 190 170 175 196 186 166 174 183 176 203 194 165 range: cells 38-146 range: cells 147-211 1 20190131030723lab_3_correlation_data.xls 1/31/2019 LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC 33 34 34 34 35 36 37 37 37 38 39 39 39 40 41 41 42 42 43 44 44 44 45 45 45 46 46 46 46 47 47 48 48 49 50 50 51 51 51 52 53 53 53 53 54 55 55 55 C D D F F F C C D D B F F C C C B F F B B C A D F C C D F A C B B C D D C C D C A A A F A A A A 2.66 2.5 1.76 1.41 2.17 2.16 1.92 2.94 2.36 2.56 3.03 1.49 1.88 2.32 1.93 2.48 2.84 2.11 2.16 2.62 2.36 2.04 3.45 1.92 1.27 2.22 2.54 2.22 1.61 3.47 2.96 2.41 3.3 3.03 2.13 1.99 1.9 2.72 2.49 2.66 3.67 3.27 3.93 1.84 3.85 3.59 3.35 3.41 2 20190131030723lab_3_correlation_data.xls 1/31/2019 LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC LAVC 55 56 56 56 58 58 58 58 59 59 59 60 60 62 63 64 64 64 64 67 68 68 69 69 71 71 71 72 72 72 73 73 73 73 73 73 75 75 75 75 77 77 78 78 78 78 80 80 F C D F A A D D A A A B C D A A B B C A A C C C C C C C C C C C C C C C A C C C C C A B B C A C 2.11 3.08 2.51 1.96 3.52 3.16 1.87 2.21 3.09 3.34 4 3.37 2.62 1.67 3.93 3.53 2.4 2.81 2.11 3.34 3.01 2.64 1.98 3.2 2.91 2.6 2.15 2.75 2.25 3.09 1.94 2.66 2.53 2.59 1.96 2.52 3.78 2.17 2.12 2.38 2.26 1.91 3.03 3.46 2.9 2.43 3.65 2.53 3 20190131030723lab_3_correlation_data.xls 1/31/2019 LAVC LAVC LAVC LAVC LAVC MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS 81 82 84 85 87 0 0 20 23 30 30 30 31 31 31 32 32 32 34 34 34 35 36 37 37 37 38 38 38 39 39 40 43 44 44 44 45 45 45 45 47 47 49 49 50 50 50 51 A B B A B F F F F C C F C F F C D F C F F D F C D D C C C C D C C B C F C C D D C C A C A B C B 3.64 3.34 2.34 3.94 2.33 1.59 2.18 1.72 1.43 2.09 3 1.84 2.11 1.09 1.17 2.3 2.59 1.57 2.73 2.23 2.05 2.07 2.1 2.32 2.25 1.77 3.09 2.77 2.45 2.63 2.87 1.94 2.38 2.87 2.48 1.86 2.98 3.15 2.86 1.96 2.61 2.46 3.61 3.13 3.96 2.35 2.17 2.75 4 20190131030723lab_3_correlation_data.xls 1/31/2019 MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS MONROE HS 53 53 53 55 56 56 57 57 57 60 60 61 62 65 65 69 69 72 72 73 78 79 B B F A A F A A B A A A A A A B C C C C C C 2.67 2.58 2.24 3.16 3.34 1.34 3.75 3.68 2.32 3.13 3.46 3.84 3.42 3.64 4 2.88 3.16 2.7 2.65 3.09 2.33 2.22 5
Statistics 101 Lab Exercise 3: Correlation This lab can be performed by one student or two in the lab or on another computer that uses Microsoft Excel as long as the student has access to the data sets required. This lab assumes a familiarity with the basic functions of the Excel application and that the student will practice the statistical functions needed to work the lab. In the last lab, we organized the data into a useful form that could be used to test hypotheses about two independent samples to determine if they were significantly similar or not. What if we want to determine simply if two variables are related at all? This calls for correlational statistical methods. In particular, in this lab, you will learn how to apply the Pearson correlation to samples of data from the original Lab 1 dataset which has been updated to included data on student grade point average (GPA). You will organize the data into arrays that can be operated on by Excel functions. You will select the functions that allow you to determine a Pearson correlation coefficient, derive the appropriate statistic, and make a decision whether to reject the null hypothesis. From this you will interpret your results. For this lab you will need the following items: Computer running Windows XP or later operating system (Mac running OS 10.6 or later) Microsoft Excel 2003 or later version Data set: Lab_3_Correlation_Data.xls (download or sent by email) Goals of the lab: 1. To become more competent in the use of the statistical functions of Microsoft Excel. 2. To make concrete use of the concepts of correlation and covariability in the context of inferential statistics for real data sets. 3. To determine and to display the data and results in a manner visually appropriate to the 2 Pearson r and r statistics. Assignment 1. Create a folder on the desktop of your PC titled: “Stat 101_[Your FirstNameLastName]_Lab3” 2. Download to that folder the data set “Lab_3_Correlation.xls” from email or Canvas. 3. Open that data set in a new Excel window. 4. Think ahead. When you are done today, be sure to email yourself a copy of your worked dataset so that you have a backup to work on at home. 5. Recall from previous labs that the data set you have opened contains three variables: SCHOOL (the school the students attended when they took the Psychology 1 class), GRADE (the letter grade the student earned in the class), and FINAL (the score the student got on the final exam). 6. To this has been added another column of data: GPA, the grade point average of each student at the time that he or she took the final exam for that Psychology 1 class. 7. This data is not yet in a form that would allow you to test hypotheses about relationships among the possible variables from the different SCHOOL populations. Also, the size of the data set is still a bit difficult to handle. We need to sample from the larger population to produce two groups of equal sample size so we can test hypotheses of covariance about them. Dr. Farris has used the RANDBETWEEN function in Excel to randomly select two samples of equal size, one from each of the LAVC and Monroe High School populations. These are not the same subjects that were selected for Lab 2. 8. 9. 10. Using this information, you will consider what hypotheses the data will allow you to test. Consider all that you know, and do not know, about these samples in making your decisions. Follow the four steps of hypothesis decision-making for the Pearson r. Show all your calculation work, or state the reasons for your decisions, at the end of each step. You may refer to your text and notes for guidance. For your chosen research question, you will print a report that follows APA style guidelines and that includes: (1) the scale of measurement; (2) provide the appropriate null hypotheses, correctly stated; (4) provide the appropriate alternate hypotheses, correctly stated; (5) provide the output from Excel (attach to your report). This will include the appropriate table including all main calculations from the equations at each step of the Pearson r method. You do not need to print all the data from the dataset, just those columns and rows that you used in your analysis; NOTE: organize your tables and charts in your Excel worksheet(s) so that when they are printed all items fall neatly within a page either in portrait or landscape page orientation; (6) provide the chart (graph) of the results (this will be different from past graphs); (7) print the output of Excel and graph. You may combine these into one Word document, if you wish; (8) type a paragraph which explicitly describes the steps you took to organize your data and to test your hypotheses; and (9) type no more that a one paragraph summary of what the output shows. Write this so clearly that an intelligent high school senior could understand it. For instance, you might have another college student who has not taken statistics read it and comment on its clarity.

Tutor Answer

Geontiry
School: Carnegie Mellon University

Hello buddy! I have attached the answer in a word document for you. :) Let me know if you have any edits or have any questions.

Title

EXCEL PROJECT

Name

Institutional Affiliations

Date

For this project, we were interested to determine whether the final grade scored
and the student grade point average (GPA) are related using alpha=0.05 in two different
schools. We obtained a random sample of 21 students in each of the two schools i.e. 21
students from LAVC and 21 students from MONROE HS. The scale of measurement used in
the two data sets for the two variables is interval scale evidenced by the variability of the
values of final score from a scale of 0 – 100 and ratio in the GPA variable. From the random
sampling, we obtained the following datasheet:
LAVC
Randomly
Selected Row #s
39
48
51
68
71
76
83
84
85
93
105
108
112
114
115
120
125
128
135
138
146

MONROE HS
FINAL GPA
18
34
36
45
46
47
51
51
52
55
60
63
64
68
68
71
73
73
77
78
87

1.95
1.76
2.16
3.45
2.22
2.96
2.72
2.49
2.66
3.41
3.37
3.93
2.11
3.01
2.64
2.15
2.66
1.96
1.91
2.9
2.33

Randomly
Selected Row #s
152
154
158
159
163
165
166
170
174
175
176
177
183
186
188
190
194
196
199
203
207

FINAL GPA
30
31
32
32
35
37
37
38
43
44
44
44
47
50
50
53
56
57
60
65
72

3
2.11
2.59
1.57
2.07
2.32
2.25
2.45
2.38
2.87
2.48
1.86
2.46
3.96
2.17
2.67
3.34
3.75
3.13
3.64
2.7

Running Head: LAB PROJECT REPORT

1

We then proceed with the hypothesis test using Pearson’s r as per the following four
steps:
Step 1 – Creating a Scatterplot of the Data
Then we obtained scatterplots using Excel in order to visually determine the
correlation between the variable (i.e. Final grade and GPA) for each date set (i.e. LAVC data
and MONROE HS data).
The scatter plot for LAVC is shown in the figure below:

FINAL

X-Y Scatter Plot for LAVC
100
90
80
70
60
50
40
30
20
10
0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

GPA

Figure 1: Scatter plot for LAVC
Based on this scatter plot in figure one above, we could tell at a glance that there
was a weaker positive relation between the final grade and the GPA.
Also, the scatter plot for MONROE HS is shown in the figure below:

LAB PROJECT REPORT

4

X-Y Scatter Plot for MONROE HS
80
70
y = 10.71x + 17.128
R² = 0.3262

60

FINAL

50
40
30
20
10
0
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

GPA

Figure 2: scatter plot for MONROE ...

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Anonymous
Thank you! Reasonably priced given the quality not just of the tutors but the moderators too. They were helpful and accommodating given my needs.

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