The level of measurement of each given variable are:
1. Ordinal
2. Nominal
3. Ratio
4. Interval
5. Ordinal
6. Nominal
7. Ratio
8. Interval
Level of measurement is used in assigning measurement to variables depending on their attributes.
There are basically four (4) levels of measurement (see image in the attachment):
1. <u>Nominal:</u> Here, values are assigned to variables just for naming and identification sake. It is also used for categorization.
- Examples of variables that fall under the measurement are: Favorite movie, Eye Color.
<u>2. Ordinal:</u> This level of measurement show difference between variables and the direction of the difference. In order words, it shows magnitude or rank among variables.
- Examples of such variables that fall under this are: highest degree conferred, birth order among siblings in a family.
<u>3. Interval Scale:</u> this third level of measurement shows magnitude, a known equal difference between variables can be ascertain. However, this type of measurement has <em>no true zero</em> point.
- Examples of the variables that fall here include: Monthly temperatures, year of birth of college students
4. Ratio Scale: This scale of measurement has a "true zero". It also has every property of the interval scale.
- Examples are: ages of children, volume of water used.
Therefore, the level of measurement of each given variable are:
1. Ordinal
2. Nominal
3. Ratio
4. Interval
5. Ordinal
6. Nominal
7. Ratio
8. Interval
Learn more about level of measurement here:
brainly.com/question/20816026
X2, or 2X or X times 2, or X•2.
Answer:
Each friend will get 5/8 (excluding Rita)
Each person will get 15/28 (including Rita)
Step-by-step explanation:
You must simply divide 3 3/4 ÷ 6 (excluding Rita)
You must simply divide 3 3/4 ÷ 7 (including Rita)
We need to substituter the values for the variables. 2(8)(2)/(6), or 32/6, or 5.3333
Answer:
<em>In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.</em>