# Statistics Concepts and Descriptive Measures

Statistics Concepts and Descriptive Measures

QNT 561

Descriptive Measures of Consumer Food Data

Qualitative or Quantitative:

When asked to choose one of the three datasets that was available for this assignment, the one that I choose was consumer food. The dataset that was chosen to complete this report has 3 columns that represent quantitative data and 2 columns that represent qualitative data; totaling the data set to have a total of 5 columns. The quantitative data includes – annual food spending, annual household income, and non-mortgage household debt. The qualitative data include – region and location that are located in columns four and five.

Level of Measurement

In columns D and E, the level of measurement belonging to the data set is the nominal level of measurement. The data that is found in the nominal level of measurement includes words, letters, alpha numeric symbols which is shown in the two columns (Complete Dissertation, 2018). Columns A, B, and C are defined as interval levels of measurement. The data found in interval levels of measurement classifies and orders the measurements and specifies that the distances between each interval on the scale are equivalent along the scale from low interval to high interval (Complete Dissertation, 2018).

Mean and Median

The mean term is described as the average of a set of numbers that is then divided by the total amount of numbers. Ex. 2,5,5,7, and 10 = 5.8; the addition of all numbers then divided by 5. The median term is described as the middle number or value between all the numbers in a group.

Columns Containing Quantitative Data (Mean and Median):

Columns A, B, and C are the groups containing quantitative data. The calculations for the mean and median for those three columns add up to be:

When reviewing the terminology of Standard deviation and variance we can see that these two forms of evaluation are widely used in areas of values that tend to complement or associate with averages. Standard deviation can be viewed as the form of data that is most commonly focused with mean or median. It is also used to help us determine exactly how far apart data sets are within the given information. Standard deviation also measures volatility and the variance helps to measure the length or span of data that given. In stating this it is also important to note that the variance is the exact same average of squared deviations that have been or are associated with the mean.

1. Column A (Annual Food Spending) the mean using the average function – 8966
2. Column A (Annual Food Spending) the median using the median function – 8932
3. Column B (Annual Household Income) the mean is – 55552
4. Column B (Annual Household Income) the median is – 54957
5. Column C (Non-Mortgage Household Debt) the mean is – 15604
6. Column C (Non-Mortgage Household Debt) the median is – 16100
7. Evaluation of Standard Deviation and Range

Columns Containing Quantitative Data (Standard Deviation):

Columns A, B, and C calculated by standard deviation and range are as followed:

Quantitative Data (Range):

1. Column A (Annual Food Spending) the standard deviation is – 3125.007986
2. Column B (Annual Household Income) the standard deviation is – 14661.36006
3. Column C (Non-Mortgage Household Debt) the standard deviation is – 8583.53913
4. References

1. Column A – (Annual Food Spending) – Minimum – 2587
1. Maximum Range – 17740
2. Column B – (Annual Household Income) – Minimum – 21647
1. Maximum Range – 96132
3. Column C – (Non-Mortgage Household Debt) – Minimum – 36374
1. Maximum Range – 0
4. Black, K. (2017). Business Statistics: For Contemporary Decision Making (9th ed.). Hoboken, NJ: John Wiley & Sons, Inc.