Use the given data to find a regression line that best fits the price-demand data for price p in dollars as a function of the demand x widgets. Here, price is the dependent variable, and demand is the independent variable. Find the regression function for price, and write it as p ( x ) = m x + b. Does it look like the regression line models the data well? Why or why not? Use the regression function you found to estimate p ( x ) for specific values of x. Give the real-world interpretation of the result of such computations in complete sentences. Be sure to include units. Find the value of x would make p ( x ) = 0. Give the real-world interpretation of the result of the computation in complete sentences. Annual widget demand x (widgets) Price per widget p ($/widget) 10 147 20 132 30 125 40 128 50 113 60 97 70 85 80 82 90 79 100 53 Enter the value for m, rounded to the nearest thousandth. Enter the values for b, rounded to the nearest whole number. Use the regression function p ( x ) = m x + b with m rounded to the nearest thousandth and b rounded to the nearest whole number. Compute p ( 45 ), rounded to the nearest whole number. Use the regression function p ( x ) = m x + b with m rounded to the nearest thousandth and b rounded to the nearest whole number. Find a value of x would make p ( x ) = 0. Round to the nearest whole number.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
- Use the given data to find a regression line that best fits the price-demand data for price p in dollars as a
function of the demand x widgets. Here, price is the dependent variable, and demand is the independent variable. Find the regression function for price, and write it as p ( x ) = m x + b. - Does it look like the regression line models the data well? Why or why not?
- Use the regression function you found to estimate p ( x ) for specific values of x. Give the real-world interpretation of the result of such computations in complete sentences. Be sure to include units.
- Find the value of x would make p ( x ) = 0. Give the real-world interpretation of the result of the computation in complete sentences.
|
Annual widget demand x (widgets) |
Price per widget p ($/widget) |
| 10 | 147 |
| 20 | 132 |
| 30 | 125 |
| 40 | 128 |
| 50 | 113 |
| 60 | 97 |
| 70 | 85 |
| 80 | 82 |
| 90 | 79 |
| 100 | 53 |
- Enter the value for m, rounded to the nearest thousandth.
- Enter the values for b, rounded to the nearest whole number.
-
Use the regression function p ( x ) = m x + b with m rounded to the nearest thousandth and b rounded to the nearest whole number.
Compute p ( 45 ), rounded to the nearest whole number.
-
Use the regression function p ( x ) = m x + b with m rounded to the nearest thousandth and b rounded to the nearest whole number.
Find a value of x would make p ( x ) = 0. Round to the nearest whole number.
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