Given that the sum of squares for error is 60 and the sum of squares for regression is 140, then the coefficient of determination is:

The coefficient of determination of the dataset is 0.7

The given parameters are:

--- sum of squared error

--- sum of squared regression

Start by calculating the sum of squared total (SST)

This is calculated using

So, we have:

The coefficient of determination (R^2) is then calculated using

So, we have:

Divide

Hence, the coefficient of determination is 0.7

Read more about coefficient of determination at:

link

Step-by-step explanation:

The coefficient of determination which is also known as the R² value is expressed as shown;

Sum of square of total (SST)= sum of square of error (SSE )+ sum of square of regression (SSR)

Given SSE = 60 and SSR = 140

SST = 60 + 140

SST = 200

Since R² = SSR/SST

R² = 140/200

R² = 0.7

Hence, the coefficient of determination is 0.7. Note that the coefficient of determination always lies between 0 and 1.

3.4 pizzas

step-by-step explanation:

given is a scatter plot showing number of pizzas sold during weeks for different issues of coupon

y represents number of pizzas sold and x number of coupons issued

y is marked verticaly and x horizontally

the trend line is given as

y =3.4x+43

this says that even when no coupon is issued pizzas sold will be 43

and rate of change of pizza sold for 1 unit of coupon = slope = 3.4

i.e. when x increases by 1 unit y increases by 3.4 units

hence for each additional coupon issued 3.4 pizzas more will be sold

45

step-by-step explanation: