Hola me ayudan ???????????
1 answer:
You might be interested in
Answer:
a)
The degrees of freedom are given by:
The p value for this case would be given by:
Since the p value is significantly lower than he significance level given we have enough evidence to reject the null hypothesis and we can conclude that the true mean is lower than 1425
b) For this case we need to find a critical value in the t distribution with 99 degrees of freedom who accumulates 0.025 of the area in the right tail and we got:
Since the calculated value is higher than the critical value we can reject the null hypothesis at the significance level provided and we can say that the true mean is higher than 1425
Step-by-step explanation:
Information given
represent the sample mean for the cubic feets of households
represent the population standard deviation
sample size
represent the value to verify
represent the significance level
t would represent the statistic
represent the p value
Part a
We want to test that the mean consumption of water per household has decreased due to the campaign by the city council, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
Since we don't know the deviation the statistic is given by:
(1)
The statistic is given by:
The degrees of freedom are given by:
The p value for this case would be given by:
Since the p value is significantly lower than he significance level given we have enough evidence to reject the null hypothesis and we can conclude that the true mean is lower than 1425
Part b
For this case we need to find a critical value in the t distribution with 99 degrees of freedom who accumulates 0.025 of the area in the right tail and we got:
Since the calculated value is higher than the critical value we can reject the null hypothesis at the significance level provided and we can say that the true mean is higher than 1425
Answer:
Check the explanation
Step-by-step explanation:
The First action is to enter the data in Excel and then save it as a csv file. Then call the data set in R using your saved path, I have made use of my computer path here in my code. please change it for yours.
Here is the code That you need to use for finding the multiple limear regression model and summary of it.
code:
data=read. csv("C:\\Users\\HP\\Desktop\\chegg. csv")
data
attach(data)
model=lm(Risk~Age+Blood.Pressure+Smoker)
summary(model)
And Here is the output:
Call:
lm(formula = Risk ~ Age + Blood.Pressure + Smoker)
Residuals:
Min 1Q Median 3Q Max
-13.1064 -1.5715 0.4225 3.4855 8.5561
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -91.75950 15.22276 -6.028 1.76e-05 ***
Age 1.07674 0.16596 6.488 7.49e-06 ***
Blood.Pressure 0.25181 0.04523 5.568 4.24e-05 ***
Smoker 8.73987 3.00082 2.912 0.0102 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.757 on 16 degrees of freedom
Multiple R-squared: 0.8735, Adjusted R-squared: 0.8498
F-statistic: 36.82 on 3 and 16 DF, p-value: 2.064e-07
The answer is now written in the notebook which i am uploading..
