5. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between TV budget and sales This R output is for a simple data set that contains, in thousands of dollars, TV budget for 200 different markets along with the Sales, in thousands of units, for each market. summary (model1) ## ## Call: ## Lm (formula = Sales - TV, data = train) ## ## Residuals: ## Min 10 Median 30 Маx ## -8.5816 -1.7845 -0.2533 2.1715 6.9345 ## ## Coefficients: Estimate Std. Error t value Pr(>|t|) ## (Intercept) 6.764098 0.607592 11.13 <2e-16 ***k ## TV 0.050284 0.003463 14.52 <2e-16 *** ## -- ## Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 #2# ## Residual standard error: 3.204 on 120 degrees of freedom ## Multiple R-squared: 0.6373, Adjusted R-squared: 0.6342 ## F-statistic: 210.8 on 1 and 120 DF, p-value: < 2.2e-16 confint(model1) ## 2.5 % 97.5 % ## (Intercept) 5.56110868 7.96708701 ## TV 0.04342678 0.05714057 a. Compute the standard deviation of errors. b. Construct a 95% confidence interval for slope B1. c. Using alpha=0.05, test whether B, is different from zero

5. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between TV budget and sales This R output is for a simple data set that contains, in thousands of dollars, TV budget for 200 different markets along with the Sales, in thousands of units, for each market. summary (model1) ## ## Call: ## Lm (formula = Sales - TV, data = train) ## ## Residuals: ## Min 10 Median 30 Маx ## -8.5816 -1.7845 -0.2533 2.1715 6.9345 ## ## Coefficients: Estimate Std. Error t value Pr(>|t|) ## (Intercept) 6.764098 0.607592 11.13 <2e-16 k ## TV 0.050284 0.003463 14.52 <2e-16 ## -- ## Signif. codes: O '' 0.001 '' 0.01 '' 0.05 '.' 0.1 '' 1 #2# ## Residual standard error: 3.204 on 120 degrees of freedom ## Multiple R-squared: 0.6373, Adjusted R-squared: 0.6342 ## F-statistic: 210.8 on 1 and 120 DF, p-value: < 2.2e-16 confint(model1) ## 2.5 % 97.5 % ## (Intercept) 5.56110868 7.96708701 ## TV 0.04342678 0.05714057 a. Compute the standard deviation of errors. b. Construct a 95% confidence interval for slope B1. c. Using alpha=0.05, test whether B, is different from zero

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

5. The following output from R presents the results from computing a linear model.
In our data example we are interested to study the relationship between TV budget and sales This R output
is for a simple data set that contains, in thousands of dollars, TV budget for 200 different markets along
with the Sales, in thousands of units, for each market.
summary (model1)
##
## Call:
## Lm (formula = Sales - TV, data = train)
##
## Residuals:
##
Min
10 Median
30
Маx
## -8.5816 -1.7845 -0.2533 2.1715
6.9345
##
## Coefficients:
Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.764098
0.607592
11.13
<2e-16 ***k
## TV
0.050284
0.003463
14.52
<2e-16 ***
## --
## Signif. codes:
O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1
#2#
## Residual standard error: 3.204 on 120 degrees of freedom
## Multiple R-squared: 0.6373, Adjusted R-squared: 0.6342
## F-statistic: 210.8 on 1 and 120 DF, p-value: < 2.2e-16
confint(model1)
##
2.5 %
97.5 %
## (Intercept) 5.56110868 7.96708701
## TV
0.04342678 0.05714057
a. Compute the standard deviation of errors.
b. Construct a 95% confidence interval for slope B1.
c. Using alpha=0.05, test whether B, is different from zero

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