The linear regression model gives mathematical relationship between two variables, the dependent<em> and </em>independent<em> variables</em>. The linear models between the <em>quantity sold and the variables average price, disposable income and monthly advertising are</em> :
- <em>y = - 4713.46x + 117763</em>
- <em>y = 2.94x - 58384</em>
- <em>y = - 1.75x - 32655</em>
To create a linear model using the data given, we use technology such as a excel or a linear regression calculator :
The linear model created using a linear regression calculator between each of the independent variables and quantity sold are :
<u>Average price of deep - dish and Quantity sold :</u>
- Average price of deep - dish pizza(X)
- Quantity sold (Y)
y = - 4713.46x + 117763
<u>Disposable income and Quantity sold</u> :
- Disposable income (X)
- Quantity sold (Y)
y = 2.94x - 58384
<u>Monthly advertising expenditure and Quantity sold</u> :
- Monthly advertising cost (X)
- Quantity sold (Y)
y = - 1.75x - 32655
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Answer: Graph C
Step-by-step explanation:
y-2 < -2x+2
y<-2x+4
slope is negative so it goes down. y int is 4.
insert (0, 0) into equation and 0 < 4 is true so shade where the origin is.
Answer:
I think A
Step-by-step explanation:
srry if wrong
i hope this helps tho
N² - 49 = 0
<u> + 49 + 49</u>
n² = 49
n = <u>+</u>7
The solution to the problem is {7, -7}.
Answer:
12.1%
Step-by-step explanation:
Given that:
Mean (μ) = 20.2 grams and standard deviation (σ) = 0.18 grams.
The z score is a score used to determine the number of standard deviations by which the raw score is above or below the mean. A positive z score means that the raw score is above the mean and a negative z score means that the raw score is below the mean. It is given by:
a) For x < 19.99 g:
From the normal distribution table, P(x < 19.99) = P(z < -1.17) = 0.1210 = 12.1%
The probability that a randomly chosen mouse has a mass of less than 19.99 grams is 12.1%
