Question
In: Statistics and Probability
The following data lists age (x, in years) and FICO credit score (y) for 15 random...
The following data lists age (x, in years) and FICO credit score (y) for 15 random credit card customers. At the 10% significance level, use Excel to test the claim that age and credit score are linearly related by specifying the slope estimate, p-value and final conclusion below. Do not round any intermediate calculations. Round your slope estimate answer to 2 decimal places. Round your p-value to 4 decimal places. Enter a "−" sign in front of any negative answer.
Slope estimate =
p-value =
Final conclusion:
The data does not support the claim that age and credit score are linearly related.
The data supports the claim that age and credit score are linearly related.
Age Credit Score
68 603
61 805
45 774
73 661
80 793
69 611
25 575
42 732
47 515
26 714
71 702
69 792
27 791
79 660
72 713
Solutions
Expert Solution
Using Excel, go to Data, Select Data Analysis, choose Regression. Put Age in X input range and Credit score in Y input range. Put confidence level = 90
| SUMMARY OUTPUT | |||||
| Regression Statistics | |||||
| Multiple R | 0.0627 | ||||
| R Square | 0.0039 | ||||
| Adjusted R Square | -0.0727 | ||||
| Standard Error | 93.1960 | ||||
| Observations | 15 | ||||
| ANOVA | |||||
| df | SS | MS | F | Significance F | |
| Regression | 1 | 445.5950 | 445.5950 | 0.0513 | 0.8243 |
| Residual | 13 | 112911.3384 | 8685.4876 | ||
| Total | 14 | 113356.9333 | |||
| Coefficients | Standard Error | t Stat | P-value | ||
| Intercept | 679.8155 | 75.6761 | 8.9832 | 0.0000 | |
| Age | 0.2854 | 1.2602 | 0.2265 | 0.8243 |
Slope estimate = 0.29
p-value (Significance F) = 0.8243
H0: Age and credit score are not linearly related
H1: Age and credit score are linearly related
p-value = 0.8243
Since p-value is more than 0.10, we do not reject the null hypothesis.
The data does not support the claim that age and credit score are linearly related.
orchestra answered 1 year agoRelated Solutions
The following are the FICO credit rating scores of 12 random people.
The following are the FICO credit rating scores of 12 random people. Use the given data values to construct a boxplot graph and identify the Five-Number Summary. Round decimals to two places. 664 693 698 714 751 753 779 789 802 818 834 836
1) Compute the sample mean.
2) Compute the sample standard deviation.
3) Compute the coefficient of variation(CV).
4) Find the Five-Number Summary:
The following data on x = score on a measure of test anxiety and y =...
The following data on x = score on a measure of test
anxiety and y = exam score for a sample of n = 9
students are consistent with summary quantities given in a paper.
Higher values for x indicate higher levels of anxiety.
Compute the value of the correlation coefficient. (Give the answer
to four decimal places.)
r = ________
x
23
13
15
0
17
21
19
14
20
y
44
59
49
76
49
51
46
52...
A Fair Isaac Corporation (FICO) score is used by credit agencies (such as mortgage companies and...
A Fair Isaac Corporation (FICO) score is used by credit agencies
(such as mortgage companies and banks) to assess the
creditworthiness of individuals. Values range from 300 to 850, with
a FICO score over 700 considered to be a quality credit risk.
According to Fair Isaac Corporation, the mean FICO score is 703.5.
A credit analyst wondered whether high-income individuals (incomes
in excess of $100,000 per year) had higher credit scores. He
obtained a random sample of 40 high-income individuals...
A Fair Isaac Corporation (FICO) score is used by credit agencies such as banks to determine...
A Fair Isaac Corporation (FICO) score is used by credit
agencies such as banks to determine whether to lend money and the
interest rate to charge. Its value ranges from 300 to 850 and if
you have a score over 700, you are considered to be a "quality"
credit risk. The mean credit score of average income earners in
California is estimated by Fair Isaac to be 664. A recent survey of
47 high income earners in California had a...
Add each of the following (X, Y) score pairs to the data in the table below....
Add each of the following (X, Y) score pairs to the data in the
table below. (Add only the single data point in each part of this
problem, so that there are always eight scores in the sample.) For
each part, sketch a scatterplot of the data with the additional
point included and compute the correlation between X and Y with the
additional point included. Compare these correlations to the one
you computed in section II question #1 above. Note...
Nast stores has derived the following consumer credit-scoring model after years of data collect Y=(0.20 x...
Nast stores has derived the following consumer credit-scoring
model after years of data collect Y=(0.20 x Employment) + (0.4 x
Homeowner) + (0.3 x Cards)
Employment = 1 if employed part-time, and 0 if unemployed
Cards= 1 if presently has 1-5 credit cards, 0 otherwise
Nast determines that a score of at least 0.70 indicates a very
good credit risk, and it extends credit to these individuals. (each
letter below is a separate question, answer a-d)
PLEASE SHOW ALL WORK...
A sales manager collected the following data on x = years of experience and y =...
A sales manager collected the following data on x =
years of experience and y = annual sales ($1,000s). The
estimated regression equation for these data is
ŷ = 83 + 4x.
Salesperson
Years of
Experience
Annual Sales
($1,000s)
1
1
80
2
3
97
3
4
102
4
4
107
5
6
103
6
8
116
7
10
119
8
10
123
9
11
127
10
13
136
(a)
Compute SST, SSR, and SSE.
SST=SSR=SSE=
(b)
Compute the coefficient...
A sales manager collected the following data on x = years of experience and y =...
A sales manager collected the following data on x =
years of experience and y = annual sales ($1,000s). The
estimated regression equation for these data is
ŷ = 82 + 4x.
Salesperson
Years of
Experience
Annual Sales
($1,000s)
1
1
80
2
3
97
3
4
102
4
4
107
5
6
103
6
8
101
7
10
119
8
10
128
9
11
127
10
13
136
(a)
Compute SST, SSR, and SSE.
SST=SSR=SSE=
(b)
Compute the coefficient...
Consider the following data for two variables, x and y. x 9 32 18 15 26...
Consider the following data for two variables, x and
y.
x
9
32
18
15
26
y
10
19
22
17
23
(a) Develop an estimated regression equation for the data of the
form ŷ = b0 +
b1x. (Round
b0 to two decimal places and
b1 to three decimal places.)
ŷ =
Comment on the adequacy of this equation for predicting
y. (Use α = 0.05.)
The high p-value and low coefficient of determination
indicate that the equation is...
Consider the following data for two variables, x and y. x 9 32 18 15 26...
Consider the following data for two variables, x and
y.
x
9
32
18
15
26
y
10
19
21
17
23
(a)Develop an estimated regression equation for the data of the
form ŷ = b0 +
b1x. (Round
b0 to two decimal places and
b1 to three decimal places.)
ŷ =____
(b)Develop an estimated regression equation for the data of the
form ŷ = b0 +
b1x +
b2x2.
(Round b0 to two decimal places and
b1 to three...
ADVERTISEMENT
ADVERTISEMENT
Latest Questions
- NPV Your division is considering two investment projects, each of which requires an up-front expenditure of...
- Please repond with a 200 word limit paragraph no copy or paste your own words!!! What...
- Participant Reactivity Think about how participants might react to environmental cues in an experimental situation and...
- Describe scenarios when you would use a Fixed-size virutal disk, a Dynamic virtual disk, and a...
- A conductive sphere with radius ? = 1 ? has been increased to the potential ?0...
- Define a char variable. Read a char value from the keyboard into the variable. Print the...
- If you wanted to store a birthday within code, what class would you use in Java?...
ADVERTISEMENT