b. An excel analysis was applied to data collected from a random sample of 100 people in order to preform regression analysis for the prediction of variable y using the values of 3 predictors: X1, X2, X3. The following output was obtained (omitting some entries). Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total X1 X2 X3 Intercept df 2.229 100 Coefficients -0.643 -0.111 0.395 0.073 SS 141.77 476.98 SE 3.400 0.084 0.082 0.047 MS F t Stat P-value -0.189 0.850485 Lower 95% -7.392 -0.278 0.233 -0.021 Upper 95% 6.107 0.056 0.558 0.166 (Note: since the sample is large, in sections where the t distribution is needed, you can use the Z one) i. Use a significance level of 1% to test if the entire regression model is significant. ii. Test which of the predictors is significant using a significance level of 1%.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Solve Solve (b part i and ii) only in 30 min and get a thumb up i need perfect soloution plz.. And show neat and clean handwriting plz
(there is no connection between the different sections)
Question 3
a. In order to check for the possible relationship between drinking orange juice and
protection against the flu virus, research was held during the winter period, which
examined a random sample of 600 people who took a daily morning cup of orange
juice (test group). In parallel, a control group of 800 randomly chosen people who
didn't drink any orange juice during this period was examined as well.
It was found that 25% of the people in the test group and 30% of the people in the
control group were infected with the flu virus.
Based on these results, test, using a 1% significance level, whether there is a
relationship between drinking orange juice and protection against the flu virus.
b. An excel analysis was applied to data collected from a random sample of 100 people
in order to preform regression analysis for the prediction of variable y using the
values of 3 predictors: X1, X2, X3.
The following output was obtained (omitting some entries).
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
X1
X2
X3
Regression
Residual
Total
Intercept
df
2.229
100
Coefficients
-0.643
-0.111
0.395
0.073
SS
141.77
476.98
SE
3.400
0.084
0.082
0.047
MS
F
t Stat
P-value Lower 95%
-0.189 0.850485
-7.392
-0.278
0.233
-0.021
Upper 95%
6.107
0.056
0.558
0.166
(Note: since the sample is large, in sections where the t distribution is needed, you can use the Zone)
i. Use a significance level of 1% to test if the entire regression model is significant.
ii. Test which of the predictors is significant using a significance level of 1%.
c. In a large class, the average grade in an economics course was 80 with SD of 10 points
and the average grade in a psychology course was 75 with SD of 5 points. Based on the
grades of the students in those two courses, a regression analysis was performed in
Transcribed Image Text:(there is no connection between the different sections) Question 3 a. In order to check for the possible relationship between drinking orange juice and protection against the flu virus, research was held during the winter period, which examined a random sample of 600 people who took a daily morning cup of orange juice (test group). In parallel, a control group of 800 randomly chosen people who didn't drink any orange juice during this period was examined as well. It was found that 25% of the people in the test group and 30% of the people in the control group were infected with the flu virus. Based on these results, test, using a 1% significance level, whether there is a relationship between drinking orange juice and protection against the flu virus. b. An excel analysis was applied to data collected from a random sample of 100 people in order to preform regression analysis for the prediction of variable y using the values of 3 predictors: X1, X2, X3. The following output was obtained (omitting some entries). Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA X1 X2 X3 Regression Residual Total Intercept df 2.229 100 Coefficients -0.643 -0.111 0.395 0.073 SS 141.77 476.98 SE 3.400 0.084 0.082 0.047 MS F t Stat P-value Lower 95% -0.189 0.850485 -7.392 -0.278 0.233 -0.021 Upper 95% 6.107 0.056 0.558 0.166 (Note: since the sample is large, in sections where the t distribution is needed, you can use the Zone) i. Use a significance level of 1% to test if the entire regression model is significant. ii. Test which of the predictors is significant using a significance level of 1%. c. In a large class, the average grade in an economics course was 80 with SD of 10 points and the average grade in a psychology course was 75 with SD of 5 points. Based on the grades of the students in those two courses, a regression analysis was performed in
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