If the value of bo is negative, then the relationship: will not cause any impact to y values regardless of how the x values change. cannot be determined by this alone causes y to increase as x increases. causes y to decrease as x increases.

hind the slopeof the secaut line between the values of = -5 and Xa = -9 X, %3D

***PLEASE INCLUDE EXCEL OUTPUT WITH YOUR RESPONSE

Suppose that y is decreasing at a rate of 4 units per 3-unit increase of x. What can we say about the slope of the linear relationship between x and y? What can we say about the intercept?

I need help with finding constants and coefficients for the equation of y hat. Please make sure to round as required in the attachment

A value of r close to plus or minus 1 indicates that there is a strong linear relationship between the variables  true or false?

The costs of various sailboats of varying lengths are xx, length in feetyy, thousands of dollars......The line of best fit for the data in this table (no data shown) is y=3.28x−22.59y=3.28x-22.59. Approximate the cost of a sailboat (to the nearest thousand) that is 30 ftftlong. $ thousand

This question has do with linear regression.If I consider attendance in a class versus final grade in the class can you explain where there is a positive and when there is a negative relationship between them.

If the equation of the regression line for hours a spent cycling in the summer and hours y spent working in the summer is ŷ = - 0.7x + 42 , then as the number of cycling hours increases, the number of work hours tends to decrease. true O false

Sometimes we have data that can be paired together, such as height and weight of an individual. When two variables are measured and paired together, we have what we call paired data. When we have paired data, we can plot the results using a scatterplot. This will give us a visual to determine if there is a relationship between the variables of interest.  For two variable relationships, we can often report the data as ordered pairs in (x, y) format. Our x-variable in this case is known as the explanatory variable, and the y-variable is known as the response variable. We use values of x to predict y.  If there appears to be a linear relationship between the two variables, we can create a least squares regression line. This is a line that “best” fits the dataset we have. The result will be a linear equation and we can use this equation to predict future values.   For simple linear regression (ordered pairs with only two variables), the general form of a regression line follows:�̂=β0+β1�,…