Regression model I need part c and d only You are required to;a. Complete the missing entries from A to L in this outputANSWER:b. Derive the regression modelANSWER:c. What does the standard error of estimate tell you about the model?ANSWER:d. Assess the independent variables significance at 5% level (develop hypothesis if necessary inthe analysis)?ANSWER: Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion.Marketing manager plans to use a multiple regression model to achieve their target. His modelconsiders yearly revenue as the dependent variable. He found that number of people within 2KM(People), Mean household income(income), no of competitors and price as explanatory variables ofcompany yearly revenue.The following is the descriptive statistics and regression output from Excel.RevenuePeopleIncomeCompetitorsPrice343985.685307.898635970.28139.084528141522.082.80.142857Mean5.88Standard Error582.137838541339.50.051030203Median345186.5803235.75#N/A5917ModeStandard DeviationSample Variance#N/A3983.47613967225.298437532.511154118.3347181.0101530.3808380270.130204082284140868939316944211.511.020408Sum171982842985132076148140Count5050505050SUMMARYOUTPUTRegression Statistics0.77Multiple RR SquareAdjusted R SquareStandard ErrorAB25139.79Observations50.00ANOVASignificanceofMSF40585378295RegressionResidualTotalH3.0831E-0828440403984GE69025780279t Stat-0.8706P-value0.38860.08910.00000.08570.1251CoefficientsStandard Error78524.72513.70510.93583818.5426-88363.1524InterceptPeople8.43947.2723-6709.4320IncomeCompetitorsPriceK12ivate Wind15988.704810219.0263LGo to Settings to

Using the given Regression output, we need to answer parts c and d

Answered: c. What does the standard error of…

c. What does the standard error of estimate tell you about the model? ANSWER: d. Assess the independent variables significance at 5% level (develop hypothesis if necessary in the analysis)? ANSWER:

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

Regression model

I need part c and d only

Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion.
Marketing manager plans to use a multiple regression model to achieve their target. His model
considers yearly revenue as the dependent variable. He found that number of people within 2KM
(People), Mean household income(income), no of competitors and price as explanatory variables of
company yearly revenue.
The following is the descriptive statistics and regression output from Excel.
Revenue
People
Income
Competitors
Price
343985.68
5307.89863
5970.28
139.0845281
41522.08
2.8
0.142857
Mean
5.88
Standard Error
582.1378385
41339.5
0.051030203
Median
345186.5
8032
3
5.75
#N/A
5917
Mode
Standard Deviation
Sample Variance
#N/A
3
983.47613
967225.2984
37532.51115
4118.334718
1.010153
0.380838027
0.130204082
284
1408689393
16944211.51
1.020408
Sum
17198284
298513
2076148
140
Count
50
50
50
50
50
SUMMARY
OUTPUT
Regression Statistics
0.77
Multiple R
R Square
Adjusted R Square
Standard Error
A
B
25139.79
Observations
50.00
ANOVA
Significance
of
MS
F
40585378295
Regression
Residual
Total
H
3.0831E-08
28440403984
G
E
69025780279
t Stat
-0.8706
P-value
0.3886
0.0891
0.0000
0.0857
0.1251
Coefficients
Standard Error
78524.7251
3.7051
0.9358
3818.5426
-88363.1524
Intercept
People
8.4394
7.2723
-6709.4320
Income
Competitors
Price
K
12ivate Wind
15988.7048
10219.0263
L
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