Question

A recent study by a senior fellow at the Wharton Business School finds strong relation between income and happiness. The new study says that money improves happiness no matter how much someone already has. A dataset contains 498 observations record the normalized income and happiness and the correlation between these two variables is 0.8656. A quick summary of the dataset is given in the following. Scatter plot of Data Summary statistics: Sample size n = 498 ?x = 4.4669, sx = 1.7375, ?i(xi - ?x)" = 1500.4436 ?y = 3.3929, sy = 1.4328, ?i(yi - ?y)" = 1020.3178 A simple linear regression was fitted to this dataset, and summary of fitted model in R: Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -2.02479 -0.48526 0.04078 0.45898 2.37805 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) A 0.08884 B 0.0219 * x C 0.01854 D <2e-16 * --- Signif. codes: 0 ‘*’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7181 on E degrees of freedom Multiple R-squared: F Adjusted R-squared: 0.7488 F-statistic: 1483 on 1 and 496 DF, p-value: < 2.2e-16

          A recent study by a senior fellow at the Wharton Business School finds strong relation between income and happiness. The new study says that money improves happiness no matter how much someone already has. A dataset contains 498 observations record the normalized income and happiness and the correlation between these two variables is 0.8656. A quick summary of the dataset is given in the following.
Scatter plot of Data

Summary statistics:
Sample size n = 498
?x = 4.4669, sx = 1.7375, ?i(xi - ?x)" = 1500.4436
?y = 3.3929, sy = 1.4328, ?i(yi - ?y)" = 1020.3178

A simple linear regression was fitted to this dataset, and summary of fitted model in R:

Call:
lm(formula = y ~ x)

Residuals:
Min 1Q Median 3Q Max
-2.02479 -0.48526 0.04078 0.45898 2.37805

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) A 0.08884 B 0.0219 *
x C 0.01854 D <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7181 on E degrees of freedom
Multiple R-squared: F Adjusted R-squared: 0.7488
F-statistic: 1483 on 1 and 496 DF, p-value: < 2.2e-16

A recent study by a senior fellow at the Wharton Business School finds strong relation between income and happiness. The new study says that money improves happiness no matter how much someone already has. A dataset contains 498 observations record the normalized income and happiness and the correlation between these two variables is 0.8656. A quick summary of the dataset is given in the following.
Scatter plot of Data

Summary statistics:
Sample size n = 498
?x = 4.4669, sx = 1.7375, ?i(xi - ?x)" = 1500.4436
?y = 3.3929, sy = 1.4328, ?i(yi - ?y)" = 1020.3178

A simple linear regression was fitted to this dataset, and summary of fitted model in R:

Call:
lm(formula = y x)

Residuals:
Min 1Q Median 3Q Max
-2.02479 -0.48526 0.04078 0.45898 2.37805

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) A 0.08884 B 0.0219 *
x C 0.01854 D <2e-16 ***
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7181 on E degrees of freedom
Multiple R-squared: F Adjusted R-squared: 0.7488
F-statistic: 1483 on 1 and 496 DF, p-value: < 2.2e-16

Added by Serenie

Question

Please give Ace some feedback