XYZ Training wants to know if they can use data from the past 12 months of numbe of monthly clients (x) and profit for each corresponding month (y) to perform a regression analysis. Examine their Excel regression printout. Identify the y intercept of the regression line. Round answers to 3 decimal places. SUMMARY OUTPUT Regression Sta tistics Multiple R 0.891759168 R Square 0.795234414 Adjusted R Square 0.774757855 Standard Error 1841.207101 Observations 12 ANOVA df SS MS F Regression 1 131656855.8 1.32E+08 38.836331 Residual 10 33900435.9 3390044 Total 11 165557291.7 Coefficients Standard Error t Stat P-value 1489.764874 2.219215 Intercept XVariable 1 3306.109257 0.050762 186.8522243 29.9832823 6.23188 9.732E-05 -3306.109 3306.109 186.852 -186.852

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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XYZ Training wants to know if they can use data from the past 12 months of number
of monthly clients (x) and profit for each corresponding month (y) to perform a
regression analysis. Examine their Excel regression printout. Identify the y intercept
of the regression line. Round answers to 3 decimal places.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.891759168
R Square
0.795234414
Adjusted R Square 0.774757855
Standard Error
1841.207101
Observations
12
ANOVA
df
SS
MS
Regression
1
131656855.8 1.32E+08 38.836331
Residual
10
33900435.9
3390044
Total
11
165557291.7
Coefficients Standard Error
t Stat
P-value
Intercept
X Variable 1
3306.109257
1489.764874 2.219215
0.050762
186.8522243
29.9832823
6.23188 9.732E-05
-3306.109
3306.109
186.852
-186.852
Transcribed Image Text:XYZ Training wants to know if they can use data from the past 12 months of number of monthly clients (x) and profit for each corresponding month (y) to perform a regression analysis. Examine their Excel regression printout. Identify the y intercept of the regression line. Round answers to 3 decimal places. SUMMARY OUTPUT Regression Statistics Multiple R 0.891759168 R Square 0.795234414 Adjusted R Square 0.774757855 Standard Error 1841.207101 Observations 12 ANOVA df SS MS Regression 1 131656855.8 1.32E+08 38.836331 Residual 10 33900435.9 3390044 Total 11 165557291.7 Coefficients Standard Error t Stat P-value Intercept X Variable 1 3306.109257 1489.764874 2.219215 0.050762 186.8522243 29.9832823 6.23188 9.732E-05 -3306.109 3306.109 186.852 -186.852
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