Below is some of the regression output from a simple regression of the average winter heating bill (expressed in 5 andthe size of the house (expressed in square feet) SUMMARY OUTPUT House size w heating bill
Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
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QUESTION9
1.
Below is some of the regression output fron a simple regression of the average winter heating bill (expressed in
andnhe size of the house (expressed in square feet)
SUMMARY OUTPUT
House size v heating bill
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
10
ANOVA
df
SS
MS
Significance F
Regression
21369.9
21369.9
30.15
0,000579857
Residual
5670.1
708.8
Total
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper
Intoreent
34 91
Dɔ 15
F.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F57d798a6-d572-4fac-a3d5-2ff9f6640ec9%2Fb99bd70b-cee5-470d-ad9f-49f2dee7bcb8%2F7zitd6d_processed.jpeg&w=3840&q=75)
![ANOVA
df
SS
MS
F
Significance Fl
Regression
21369 9
21369.9
30.15
0.000579857
Residual
5670.1
708.8
Total
Coefficients
34.81
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
55.49
0.63
0.548
-93.15
162.78
House Sizel
0.12
0.022
0.001
3.
Based on the regression output, what is the predicted heating bill for a house that is 2500 square feet? (please
express your answer using 2 decimal places)
4.
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