Description
Part C: Regression and Correlation Analysis
Use the dependent variable (labeled Y) and the independent variables (labeled X1, X2, and X3) in the data file. Use Excel to perform the regression and correlation analysis to answer the following.
- Generate a scatterplot for the specified dependent variable (Y) and the X1 independent variable, including the graph of the "best fit" line. Interpret.
- Determine the equation of the "best fit" line, which describes the relationship between the dependent variable and the selected independent variable.
- Determine the coefficient of correlation. Interpret.
- Determine the coefficient of determination. Interpret.
- Test the utility of this regression model. Interpret results, including the p-value.
- Based on the findings in Steps 1-5, analyze the ability of the independent variable to predict the designated dependent variable.
- Compute the confidence interval for β1 (the population slope) using a 95% confidence level. Interpret this interval.
- Using an interval, estimate the average for the dependent variable for a selected value of the independent variable. Interpret this interval.
- Using an interval, predict the particular value of the dependent variable for a selected value of the independent variable. Interpret this interval.
- What can be said about the value of the dependent variable for values of the independent variable that are outside the range of the sample values? Explain.
In an attempt to improve the model, use a multiple regression model to predict the dependent variable, Y, based on all of the independent variables, X1, X2, and X3.
- Using Excel, run the multiple regression analysis using the designated dependent and three independent variables. State the equation for this multiple regression model.
- Perform the Global Test for Utility (F-Test). Explain the conclusion.
- Perform the t-test on each independent variable. Explain the conclusions and clearly state how the analysis should proceed. In particular, which independent variables should be kept and which should be discarded. If any independent variables are to be discarded, re-run the multiple regression, including only the significant independent variables, and summarize results with discussion of analysis.
- Is this multiple regression model better than the linear model generated in parts 1-10? Explain.
- NO plagiarism
Summarize your results from Steps 1–14 in a three-page report. The report should explain and interpret the results in ways that are understandable to someone who does not know statistics.
Submission: The summary report and all of the work done in 1–14 (Excel output and interpretations) as an appendix
Format for report:
- Summary Report
- Points 1–14 should be addressed with appropriate output, graphs, and interpretations. Be sure to number each point 1–14.
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Explanation & Answer
Hi,I showed my work both on excel and appendix.Let me know if you have any doubt.Thankyou.
CALLS(X1) SALES(Y)
171
46
134
34
165
44
186
45
180
42
184
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126
33
172
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161
42
149
37
181
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145
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198
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149
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168
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124
32
149
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135
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185
49
154
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149
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193
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153
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152
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170
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174
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164
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139
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171
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170
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183
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169
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143
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167
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168
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189
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149
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170
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Simple Linear Regression Analysis
Regression Statistics
Multiple R
0,8713
R Square
0,7592
Adjusted R Square 0,7568
Standard Error
2,0571
Observations
100
Calculations
b1, b0 Coefficients
b1, b0 Standard Error
R Square, Standard Error
F , Residual df
Regression SS , Residual SS
Confidence level
t Critical Value
Half Width b0
Half Width b1
0,2018
0,0115
0,7592
309,0460
1307,7471
95%
1,9845
3,7140
0,0228
9,6380
1,8716
2,0571
98,0000
414,6929
Employee Number
1
2
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SALES(Y)
46
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52
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CALLS(X1)
171
134
165
186
180
184
126
172
161
149
181
145
140
198
149
168
124
149
135
185
154
149
193
153
152
170
192
165
150
174
168
178
164
191
139
138
171
170
153
154
144
134
177
157
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129
183
TIME(X2)
12,7
17,0
15,7
13,0
14,8
12,4
20,2
14,4
13,9
15,4
12,2
16,0
17,0
13,5
17,3
12,9
17,5
16,5
18,2
18,6
18,3
15,...