Please answer question 10.1 Now that you have looked at the descriptive side of the regression analysis, let's turn to whatinferences can be drawn from this analysis. Recall that we have a random sample of 80countries. For each country we recorded the overall happiness score (out of 10 points) alongwith the life expectancy (in years).Call:1m(formula= Happiness.scoreLife.expectancy, data =happiness)Residuals:Min1Q Median3QМаx-1. 3949 -0.5823 0.0187 0.5864 1.2539Coefficients:Estimate Std. Error t value Pr (>|t|)(Intercept)Life.expectancy-2.55960.8112-3.160.0023 **0.12280.01239.951.6e-15 ***Signif. codes: 0 ***' 0.001 “**' 0.01 “*' 0.05 '.' 0.1 ' ' 1Residual standard error: 0.72 on 78 degrees of freedomMultiple R-squared: 0.559,F-statistic: 98.9 on 1 and 78 DF, p-value: 1.6e-15Adjusted R-squared: 0.553in 15Min1stQu.MedianMean3rd Qu.МаxLife Expectancy50.8361.6666.7065.3969.6676.82Happiness Score2.5234.8845.3975.4696.1707.620 You are asked to provide the following two items to be included as part of a report:A 90% confidence interval to estimate the average increase in happiness score for every oneyear increase in life expectancy for all countries in the world.Using the resulting 90% confidence interval, is there sufficient evidence to suggest that ahigher life expectancy is associated with a higher happiness score, on average. Explain why.You may include your answer through either typing your response in the box provided oruploading a file, but do not include both. Be sure to show all work.Enter your answer hereORE Please select file(s)Select file(s)

Name: Please answer question 10.1 Now that you have looked at the descriptiv
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Description: Please answer question 10.1 Now that you have looked at the descriptive side of the regression analysis, let's turn to whatinferences can be drawn from this analysis. Recall that we have a random sample of 80countries. For each country we recorded th

Here we have given the regression output of the regression analysis of happiness score on life…

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Now that you have looked at the descriptive side of the regression analysis, let's turn to what inferences can be drawn from this analysis. Recall that we have a random sample of 80 countries. For each country we recorded the overall happiness score (out of 10 points) along with the life expectancy (in years). Call: 1m(formula = Happiness.score Life.expectancy, data happiness Residuals: 1Q Median -1.3949 -0.5823 0.0187 0.5864 Min 3Q 1.2539 Max Coefficients: (Intercept) Life.expectancy Estimate Std. Error t value Pr(>|t|) 0.0023 ** 1.6е-15 *** -2.5596 0.8112 -3.16 0.1228 0.0123 9.95 --- Signif. codes: 0 ****'0.001 “**' 0.01 **' 0.05 '.' 0.1 “ ' 1 Residual standard error: 0.72 on 78 degrees of freedom Multiple R-squared: 0.559, F-statistic: 98.9 on 1 and 78 DF, p-value: 1.6e-15 Adjusted R-squared: 0.553 Min 1st Qu. Median Mean 3rd Qu. Мах Life Expectancy 50.83 61.66 66.70 65.39 69.66 76.82 Happiness Score 2.523 | 4.884 5.397 5.469 6.170 7.620

Now that you have looked at the descriptive side of the regression analysis, let's turn to what inferences can be drawn from this analysis. Recall that we have a random sample of 80 countries. For each country we recorded the overall happiness score (out of 10 points) along with the life expectancy (in years). Call: 1m(formula = Happiness.score Life.expectancy, data happiness Residuals: 1Q Median -1.3949 -0.5823 0.0187 0.5864 Min 3Q 1.2539 Max Coefficients: (Intercept) Life.expectancy Estimate Std. Error t value Pr(>|t|) 0.0023 1.6е-15 * -2.5596 0.8112 -3.16 0.1228 0.0123 9.95 --- Signif. codes: 0 '0.001 “' 0.01 ' 0.05 '.' 0.1 “ ' 1 Residual standard error: 0.72 on 78 degrees of freedom Multiple R-squared: 0.559, F-statistic: 98.9 on 1 and 78 DF, p-value: 1.6e-15 Adjusted R-squared: 0.553 Min 1st Qu. Median Mean 3rd Qu. Мах Life Expectancy 50.83 61.66 66.70 65.39 69.66 76.82 Happiness Score 2.523 | 4.884 5.397 5.469 6.170 7.620

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Topic Video

Question

Please answer question 10.1

Now that you have looked at the descriptive side of the regression analysis, let's turn to what
inferences can be drawn from this analysis. Recall that we have a random sample of 80
countries. For each country we recorded the overall happiness score (out of 10 points) along
with the life expectancy (in years).
Call:
1m(formula
= Happiness.score
Life.expectancy, data =
happiness)
Residuals:
Min
1Q Median
3Q
Маx
-1. 3949 -0.5823 0.0187 0.5864 1.2539
Coefficients:
Estimate Std. Error t value Pr (>|t|)
(Intercept)
Life.expectancy
-2.5596
0.8112
-3.16
0.0023 **
0.1228
0.0123
9.95
1.6e-15 ***
Signif. codes: 0 ***' 0.001 “**' 0.01 “*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.72 on 78 degrees of freedom
Multiple R-squared: 0.559,
F-statistic: 98.9 on 1 and 78 DF, p-value: 1.6e-15
Adjusted R-squared: 0.553
in 15
Min
1st
Qu.
Median
Mean
3rd Qu.
Маx
Life Expectancy
50.83
61.66
66.70
65.39
69.66
76.82
Happiness Score
2.523
4.884
5.397
5.469
6.170
7.620

You are asked to provide the following two items to be included as part of a report:
A 90% confidence interval to estimate the average increase in happiness score for every one
year increase in life expectancy for all countries in the world.
Using the resulting 90% confidence interval, is there sufficient evidence to suggest that a
higher life expectancy is associated with a higher happiness score, on average. Explain why.
You may include your answer through either typing your response in the box provided or
uploading a file, but do not include both. Be sure to show all work.
Enter your answer here
OR
E Please select file(s)
Select file(s)

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