In an effort to improve a statistics course, we want to interview randomly chosen students about their attitudes toward the class. There are 800 students currently enrolled, and six instructors who teach sections of the course. To make sure that students from each instructor are equally represented in the sample, we will first take random samples of size 10 from each of the six classes separately, and then combine these to get the overall sample of students. This type of sample is called a random sample.

This type of sample is called a stratified random sample.

Step-by-step explanation:

Stratified random sampling involves dividing a population into sub-groups(strata) based on characteristics, selecting random samples of the same size from each subgroup and finally combining this random samples. Stratified random sampling is usually used for heterogeneous or dissimilar populations. It is accurate than simple random sampling and saves time time since only a smaller sample size is used.

76%

step-by-step explanation:

24% are dogs and the remaining is 76% because 100-24=76

look at the picture.

y-intercept for the function on a picture is 4 → point (0, 4).

y-intercept for the function

is for x = 0. substitute:

y-intercept is 8 → point (0, 8).