Question 3: Using Microsoft Excel: (Excel file Assignment10.xlsx) a. Calculate the mean and sample standard deviation of both the independent and dependent variables. b. Calculate the correlation coefficient, r, using Excel's CORREL function. Explain what it means. c. Calculate the coefficient of determination, r2, using Excel's RSQ function. Explain what it means. a very high score.

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Question 3: Using Microsoft Excel: (Excel file Assignment10.xlsx)
Calculate the mean and sample standard deviation of both the independent and dependent
variables.
а.
b. Calculate the correlation coefficient, r, using Excel's CORREL function. Explain what it means.
Calculate the coefficient of determination, r2, using Excel's RSQ function. Explain what it means. a
very high score.
с.
Report your results here:
Paste your Excel table here using Word's Paste Special function.
d. Coefficient of correlation means:
Interpret the coefficient of correlation using this table.
Negative
Positive
Meaning
0.00 to -0.099
0.00 to -.099
No Correlation
-0.10 to -0.299
0.10 to 0.299
Small Correlation
-0.30 to -0.499
0.30 to 0.499
Medium Correlation
-0.50 to -1.000
0.50 to 1.000
Large Correlation
e. Coefficient of determination means:
Assignment8 Spring22 ndf
Page 4 of6
Transcribed Image Text:Question 3: Using Microsoft Excel: (Excel file Assignment10.xlsx) Calculate the mean and sample standard deviation of both the independent and dependent variables. а. b. Calculate the correlation coefficient, r, using Excel's CORREL function. Explain what it means. Calculate the coefficient of determination, r2, using Excel's RSQ function. Explain what it means. a very high score. с. Report your results here: Paste your Excel table here using Word's Paste Special function. d. Coefficient of correlation means: Interpret the coefficient of correlation using this table. Negative Positive Meaning 0.00 to -0.099 0.00 to -.099 No Correlation -0.10 to -0.299 0.10 to 0.299 Small Correlation -0.30 to -0.499 0.30 to 0.499 Medium Correlation -0.50 to -1.000 0.50 to 1.000 Large Correlation e. Coefficient of determination means: Assignment8 Spring22 ndf Page 4 of6
Death Rate per 100,000 people and Donald Trump's share of the popular vote.
A group of political activists noticed that hesitancy to get the vaccine for COVID-19
seems particularly strong in states that Donald Trump won in the 2020 presidential
election. They wondered whether there is a correlation between the COVID-19 death
rate per 100,000 people per state and Donald Trump's share of the popular vote in
2020 per the 50 states and District of Columbia. The death rate data was sorted from a
Johns Hopkins University/CNN report. The Voting data was sourced from CNN.
Trump's
Per Capita Share of
COVID
Popular
Vote
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Death
330
62.0%
121
52.8%
308
49.0%
288
62.4%
189
34.3%
The data are on the right. The data are located in Assignment8_Spring22.xlsx.
162
41.9%
250
39.2%
224
39.8%
Question 1: A Priori Statistical Power
Before collecting the data, they decided to calculate a priori statistical power to
determine how large a sample is needed to achieve 80% statistical power.
5.4%
51.2%
170
287
288
Georgia
Hawaii
Idaho
linois
Indiana
49.2%
73
34.3%
221
63.8%
G*Power
228
40.6%
Conduct an a priori power analysis using G*Power. (If you are not using a Windows or
Macintosh computer use Statistics Kingdom to estimate sample size) Here are the
setting for G*Power:
Test family: t tests
Statistical test: Correlation: Point biserial model
262
57.0%
53.1%
236
230
lowa
56.1%
Kansas
Kentucky
Louisiana
Maine
247
62.1%
319
58.5%
99
44.0%
Type of power analysis: A priori: Compute required sample size..
Tails: Two
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
186
32.1%
282
258
172
32.1%
47.8%
45.3%
Effect size |p|: 0.40
a err prob: 0.05
Power (1-B prob): 0.8
346
57.5%
253
53.8%
256
53.9%
a. What is the required sample size?
164
58.2%
161
47.7%
127
45.4%
320
41.3%
b. What level of statistical power will be achieved? Round off your answer to
New Mexico
New York
North Carolina
North Dakota
257
43.5%
four digits passed the decimal point.
296
179
37.7%
49.9%
255
65.1%
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
227
53.3%
Using data from 50 states and the District of Columbia, do they have a
sufficiently large sample size to achieve 80% power assuming that the
coefficient of correlation is the population equal 0.40? Please an "X" in the
appropriate box:
302
65.4%
124
40.4%
263
48.8%
277
38.6%
277
55.1%
Yes
No
266
61.8%
253
60.7%
Техas
255
52.1%
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
111
58.1%
Statistics Kingdom
If you are using a Chromebook, conduct an a priori power analysis using the tool
found at https://www.statskingdom.com/sample size regression.html.
Here are the settings:
Type: Regression
Power: 0.8
66
30.7%
173
44.0%
123
38.8%
276
173
68.6%
48.8%
Wyoming
247
69.9%
Effect: Medium
Effect Size: 0.1615
Significance level (a): 0.05
Predictors (p): 1
Effect Type: R2/n?
Transcribed Image Text:Death Rate per 100,000 people and Donald Trump's share of the popular vote. A group of political activists noticed that hesitancy to get the vaccine for COVID-19 seems particularly strong in states that Donald Trump won in the 2020 presidential election. They wondered whether there is a correlation between the COVID-19 death rate per 100,000 people per state and Donald Trump's share of the popular vote in 2020 per the 50 states and District of Columbia. The death rate data was sorted from a Johns Hopkins University/CNN report. The Voting data was sourced from CNN. Trump's Per Capita Share of COVID Popular Vote State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Death 330 62.0% 121 52.8% 308 49.0% 288 62.4% 189 34.3% The data are on the right. The data are located in Assignment8_Spring22.xlsx. 162 41.9% 250 39.2% 224 39.8% Question 1: A Priori Statistical Power Before collecting the data, they decided to calculate a priori statistical power to determine how large a sample is needed to achieve 80% statistical power. 5.4% 51.2% 170 287 288 Georgia Hawaii Idaho linois Indiana 49.2% 73 34.3% 221 63.8% G*Power 228 40.6% Conduct an a priori power analysis using G*Power. (If you are not using a Windows or Macintosh computer use Statistics Kingdom to estimate sample size) Here are the setting for G*Power: Test family: t tests Statistical test: Correlation: Point biserial model 262 57.0% 53.1% 236 230 lowa 56.1% Kansas Kentucky Louisiana Maine 247 62.1% 319 58.5% 99 44.0% Type of power analysis: A priori: Compute required sample size.. Tails: Two Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey 186 32.1% 282 258 172 32.1% 47.8% 45.3% Effect size |p|: 0.40 a err prob: 0.05 Power (1-B prob): 0.8 346 57.5% 253 53.8% 256 53.9% a. What is the required sample size? 164 58.2% 161 47.7% 127 45.4% 320 41.3% b. What level of statistical power will be achieved? Round off your answer to New Mexico New York North Carolina North Dakota 257 43.5% four digits passed the decimal point. 296 179 37.7% 49.9% 255 65.1% Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee 227 53.3% Using data from 50 states and the District of Columbia, do they have a sufficiently large sample size to achieve 80% power assuming that the coefficient of correlation is the population equal 0.40? Please an "X" in the appropriate box: 302 65.4% 124 40.4% 263 48.8% 277 38.6% 277 55.1% Yes No 266 61.8% 253 60.7% Техas 255 52.1% Utah Vermont Virginia Washington West Virginia Wisconsin 111 58.1% Statistics Kingdom If you are using a Chromebook, conduct an a priori power analysis using the tool found at https://www.statskingdom.com/sample size regression.html. Here are the settings: Type: Regression Power: 0.8 66 30.7% 173 44.0% 123 38.8% 276 173 68.6% 48.8% Wyoming 247 69.9% Effect: Medium Effect Size: 0.1615 Significance level (a): 0.05 Predictors (p): 1 Effect Type: R2/n?
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