To test whether extracurricular activity is a good predictor of college success, a college administrator records whether students participated in extracurricular activities during high school and their subsequent college freshman GPA.
|
Extracurricular Activity |
College Freshman GPA |
|---|---|
| Yes | 3.47 |
| Yes | 3.34 |
| Yes | 3.93 |
| Yes | 3.67 |
| No | 2.92 |
| No | 3.88 |
| No | 3.47 |
| No | 2.71 |
| No | 3.83 |
| No | 2.79 |
(a) Code the dichotomous variable and then compute a
point-biserial correlation coefficient. (Round your answer to three
decimal places.)
(b) Using a two-tailed test at a 0.05 level of significance, state
the decision to retain or reject the null hypothesis.
Hint: You must first convert r to a
t-statistic.
Retain the null hypothesis
or
Reject the null hypothesis.
When Y is continuous and X is dichotomous. In this case, Pearson r, computed in the usual fashion, becomes the point-biserial correlation coefficient.
using ms-excel the correlation coefficient between x and y is 0.381 , here Yes=1 and No=0
( correlation coefficient between x and y is -10.381 , if we take Yes=0 and No=1)
| Activity(x) | Freshman GPA (y) | Activity(x) |
| Yes | 3.47 | 1 |
| Yes | 3.34 | 1 |
| Yes | 3.93 | 1 |
| Yes | 3.67 | 1 |
| No | 2.92 | 0 |
| No | 3.88 | 0 |
| No | 3.47 | 0 |
| No | 2.71 | 0 |
| No | 3.83 | 0 |
| No | 2.79 | 0 |
(b) Retain the null hypothesis ( that population correlation coefficient is zero)
null hypothesis H0:
=0
and alternate hypothesis H0:
0
we use t-test here and statistic t =r/sqrt[(1—r2)/(n—2)]=0.382/sqrt(1-0.381*0.381)/(10-1))=0.0459 with n-1=10-1=9 df
the two tailed critical t(0.05,9)=2.2622 is more than calculated t=0.0459, so we fail to reject ( or accept ) null hypothesis.
To test whether extracurricular activity is a good predictor of college success, a college administrator records...
To test whether extracurricular activity is a good predictor of college success, a college administrator records whether students participated in extracurricular activities during high school and their subsequent college freshman GPA College Freshman GPA 3.55 Yes Yes Yes 3.36 3.93 Yes 3.65 NO NO 3.85 NO 3.45 No 2.75 No NO 2.80 (a) Code the dichotomous variable and then compute a point biserial correlation coefficient. (Round your answer to three decimal places.) (b) Using a two-tailed test at a 0.05...
To test whether extracurricular activity is a good predictor of college success, a college administrator records whether students participated in extracurricular activities during high school and their subsequent college freshman GPA. Yes Yes Yes 3.49 34 3.95 3.72 2.95 3.83 3.43 2.73 3.85 2.85 No No No No No (a) Code the dichotomous variable and then compute a point-biserial correlation coefficient. (Round your answer to three decimal places.)
Extracurricular Activit Yes Yes Yes Yes No No No No No No College Freshman GPA 3.56 3.34 3.95 3.72 2.93 3.80 3.40 2.78 3.84 2.78 (a) Code the dichotomous variable and then compute a point-biserial correlation coefficient. (Round your answer to three (b) Using a two-tailed test at a 0.05 level of significance, state the decision to retain or reject the null hypothesis. Hint: Yo O Retain the null hypothesis. O Reject the null hypothesis You may need to use...
A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. school GPA are expected to do better in college. Students with higher High colgpa-Grade point average in college (Range 0.85 -3.97) hsgpaHigh school GPA (Range 2.29-4.5) Model 1: OLS, N-427 Dependent variable: colgpa coefficient std. error const hsgpa 0.5 0.15 R-squared: 0.854880 a. (3%) Write the equation for the least-squares regression line: y- b. (396) The null hypothesis is:...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA Students with higher High school GPA are expected to do better in college. colgpa = Grade point average in college (Range 0.85 -3.97) hsgpa = High school GPA (Range 2.29 -4.5) Expand Model 1: OLS, N=427 Dependent variable: colgpa coefficient std. error const 0.8 hsgpa 0.6 0.1 R-squared: 0.854880 a. Write the equation for the least squares...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High school GPA are expected to do better in college. colgpa Grade point average in college (Range 0.85-3.97) hsgpa High school GPA (Range 2.29-4.5) Model 1: OLS, N-427 Dependent variable: colgpa std. error 0.8 0.5 const hsgpa 0.1 R-squared: 0.854880 ,- a. (3%) write the equation for the least-squares regression line: b. (3%) The...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High colgpa-: Grade point average in college (Range 085-397 ) hsgpa High school GPA (Range 2.29-4.5) school GPA are expected to do better in college. Model 1: OLS, N 427 Dependent variable: colgpa coefficient 0.9 0.4 std. error const hsgpa 0.15 R-squared: 0.854880 a. (3%) Write the ea ation for the least-squares regression line:...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High school GPA are expected to do better in college colgpa Grade point average in college (Range 0.85 3.97) hsgpa High school GPA (Range 2.29-4.5) Model 1: OLS, N -427 Dependent variable: colgpa coefficient 0.9 0.4 std. error const hsgpa 0.15 R-squared: 0.854880 a. (3%) Write the equation for the least-squares regression line: y-...
SAT
Income
GPA
1651
47000
2.79
1581
34000
2.97
1790
90000
3.48
1626
60000
2.5
1754
113000
2.92
1754
71000
3.76
1706
105000
2.8
1765
59000
3.26
1786
50000
3.89
1686
27000
3.67
1790
107000
3.31
1707
109000
3.16
1804
81000
3.73
1712
62000
3.21
1607
72000
2.8
1738
63000
3.7
1790
55000
3.86
1796
64000
3.91
1547
47000
2.63
1692
89000
2.98
1711
42000
3.45
1689
70000
3.06
1740
118000
2.88
1940
113000
3.96
5 A researcher studies the...
The subjects in the data are college students. In the data, id is student ID, anxiety is student’s anxiety score via Anxiety Scale, selfest is student’s self-esteem score via Rosenberg Self-esteem Scale, GPA is student’s GPA; for gender, 0=female, 1=male; for grade, 1=freshman, 2=junior, 3=senior. We have known that population mean for Anxiety Scale is μ=60 with σ=10. Raise relevant questions ( 2 questions is fine) about the data extensively, the questions can be either about descriptive analysis or inferential...