PLEASE HELP ME! ANSWER THE QUESTION BELOW PLEASE AND THANKYOU I WILL MARK BRAINLIEST FOR BEST ANSWER!! also please write it like
2 answers:
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Answer:
The monthly payment is $262.95
Step-by-step explanation:
* Lets explain how to solve the problem
- Omar has decided to purchase an $11,000 car
- He plans on putting 20% down toward the purchase
* Lets find the value of the 20%
∵ The principal value is $11000
∴ the value of the 20% = 20/100 × 11000 = 2200
∴ He will put $2200 down
* Lets find the balance to be paid off on installments
∴ The balance = 11000 - 2200 = 8800
- He financing the rest at 4.8% interest rate for 3 years
* Lets find the rule of the monthly payment
∵ , where
- pmt is the monthly payment
- P = the investment amount
- r = the annual interest rate (decimal)
- n = the number of times that interest is compounded per unit t
- t = the time the money is invested or borrowed for
∵ P = 8800
∵ r = 4.8/100 = 0.048
∵ n = 12
∵ t = 3
∴
∴
* The monthly payment is $262.95
Answer:
Step-by-step explanation:
Hello!
Given the data for the variables:
Y: Selling price of a house on the shore of Tawas Bay
X₁: Number of bathrooms of a house on the shore of Tawas Bay.
X₂: Square feet of a house on the shore of Tawas Bay.
X₃: Number of bedrooms of a house on the shore of Tawas Bay.
The multiple regression model is Y= α + β₁X₁ + β₂X₂ + β₃X₃ + εi
a. Using software I've entered the raw data and estimated the regression coefficients:
^α= a= -5531.01
Represents the mean selling price of the houses when 0 bathrooms, 0 square feet and 0 bedrooms.
^β₁= b₁= -1386.21
Represents the modification of the mean selling price of the houses when the number of bathrooms increases in one unit and the square feet and number of bedrooms remain unchanged.
^β₂= b₂= 60.28
Represents the modification of the mean selling price of the houses when the square feet increase in one unit and the number of bathrooms and bedrooms remain unchanged.
^ β₃= b₃= 54797.08
Represents the modification of the mean selling price of the houses when the number of bedrooms increase in one unit and the number of bathrooms and square feet of the houses remain unchanged.
^Y= -5531.01 -1386.21X₁ + 60.28X₂ + 54797.08X₃
b)
R²= 0.55
R²Aj= 0.49
The coefficient of determination gives you an idea of how much of the variability of the dependent variable (Y) is due to the explanatory variables. Each time you add another explanatory variable to the regression the coefficient increases regarding of real contribution of the new variable. This could lead to thinking (wrongly) that the new variables are good to explain the dependent variable.
The adjusted coefficient of determination is a correction made to the raw coefficient of determination to have a more unbiased estimation of the effect the independent variables have over the dependent variable.
⇒ As you can see both coefficient are around 50%, which means that these explanatory variables
c)
The standard error estimate, this is the estimate of the population variance of the errors. In the ANOVA is represented by the Mean Square of the errors (MME)
Se²= MME= 3837640577.01
Se= 61948.6931
d) and f)
For the hypotheses tests for each slope the t- and p-values are:
α: 0.05
β₁: t= -0.06; p-value: 0.9528 ⇒ Do not reject H₀, the test is not significant.
β₂: t= 2.56; p-value: 0.0180 ⇒ Reject H₀, the test is significant.
β₃: t= 2.28; p-value: 0.0326 ⇒ Reject H₀, the test is significant.
e)
H₀: β₁= β₂= β₃
H₁: At least one βi is different from the others ∀ i=1, 2, 3
α: 0.05
F= 9.03
p-value: 0.0004
⇒ Reject H₀, the test is significant.
I hope it helps!