The following table represents a sample of sale prices, in thousands of dollars, and number of
1 answer:
Correct question
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
A.) state the linear regression function that estimates the number of new homes available at a specific price.
B.) state the correlation Coefficient of the data, and explain what it means in the context of the problem
Answer:
Y = -0.79X + 249.86
R = -0.9543
Step-by-step explanation:
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
Calculate the Linear regression equation :
Using the linear regression calculator :
The linear regression equation is :
Y = -0.79X + 249.86
The correlation Coefficient 'R' measures the strength of statistical relationship between the relative movement of two variables. The The value of R is -0.9543 in the question above.
This is a strong negative correlation, which means that high sales price of homes scores correlates with low number of new homes scores (and vice versa). Homes with high sales price have fewer number of new homes.
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Step-by-step explanation:
<h3> <em>The complete exercise is:"A gardener has 27 tulip bulbs, 45 tomato plants, 108 rose bushes, and 126 herb seedlings to plant in the city garden. He wants each row of the garden to have the same number of each kind of plant. What is the greatest number of rows that the gardener can make if he uses all the plants?"</em></h3><h3 />The first step to solve the exercise is to find the Greatest Common Factor (GCF) between 27, 45, 108 and 126.
You can follow these steps in order to find the GCF:
1. You must decompose 27, 45, 108 and 126into their prime factors:
2. You must multiply the commons with the lowest exponents. Then:
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Therefore, the greatest number of rows that the gardener can make if he uses all the plants is: