Can someone please double check my answers (My answers are in Capital)
1. If you produce a million products a day and you want to test ten of them to determine if they are up to standard, which ones will you choose?
a. The second ten
B. TEN RANDOM UNITS
c. The last ten
d. The first ten
2. What is the first step in solving any type of research problem?
A. COLLECTING DATA
b. Analyzing data
c. Plotting trend lines
d. Plotting graphs
3. What type of variable can inappropriately affect extrapolate data?
a. Independent variable
b. Lurking variable
c. Obvious variable
d. Dependent variable
(I'm not sure about this one
4. When taking a power function and transforming it into a linear function, you get y=ax^b and is equivalent to log y=(log a + b + x)
FALSE
5. Exponential data follows the equation y = ab^x
TRUE
6. One type of data set that is non-linear in nature is the exponential function
TRUE
Let's go through each question and explain how to get the answers:
1. If you produce a million products a day and you want to test ten of them to determine if they are up to standard, which ones will you choose?
To determine which products to choose, you need to consider randomness and representativeness. The best option would be to choose TEN RANDOM UNITS (in capital letters), as this will ensure that your sample is representative of the entire production. Picking the second ten, the last ten, or the first ten might introduce bias into your sample.
2. What is the first step in solving any type of research problem?
The first step in solving any type of research problem is COLLECTING DATA (in capital letters). Before any analysis or interpretation can take place, you need to have relevant and reliable data to work with. Collecting data is the foundation of any research process.
3. What type of variable can inappropriately affect extrapolate data?
In this question, the variable that can inappropriately affect extrapolate data is the LURKING VARIABLE (in capital letters). A lurking variable is a hidden or unaccounted-for variable that could influence the relationship between the independent and dependent variables. It can introduce bias or confounding effects when trying to extrapolate or generalize the results to the population.