Mid-term

Question 1

what is Data

Answer 1

the facts and figures collected, analyzed, and summarized for presentation and
interpretation

Question 2

What is Quantitative data

Answer 2

Data are considered quantitative data if numeric and arithmetic operations, such as addition,
subtraction, multiplication, and division, can be performed.

Question 3

What is categorical Data

Answer 3

If arithmetic operations cannot be performed, they are categorical data.

When data is seperated into groups/classes like in a Venn Diagram

Question 4

Variable

Answer 4

A variable is a characteristic or a quantity of interest that can take on different values.

Question 5

what is Variation

Answer 5

Variation is the difference in a variable measured over observations.

Question 6

What is cross sectional data

Answer 6

Cross-sectional data are data collected from several entities at the same, or approximately the same, point in time

Question 7

What is time series data

Answer 7

Time series data are data collected over
several time periods.

Question 8

What is Random Sampling

give an example

Answer 8

Random sampling is a sampling method that allows gathering a representative sample
from the POPULATION DATA

Example: Age, education, location income

Question 9

Sample data

Answer 9

A sample is a subset of the population, which consists of all the elements of interest

leads to uncertainty

Example: What is your sample? (816 voters)

Question 10

Experimental study

Answer 10

a variable of interest is first identified.

Then, one or more other variables are identified and controlled or manipulated to obtain data about how they influence the variable of interest.

Question 11

Statistical data

Answer 11

Data necessary to analyze a business problem can often be obtained with a statistical study

Statistical studies can be classified as experimental or observational

Question 12

Observational or Nonexperimental Study

Answer 12

does not attempt to control the variables of
interest.

A survey is perhaps the most common type of observational study.

Question 13

Frequency distribution

Answer 13

frequency distribution is a summary of data showing the number (frequency) of observations
in several nonoverlapping classes, typically referred to as bins.

example: 50 soft drinks are distributed over 5 types of soft drinks

Question 14

Relative Frequency

State equation

Answer 14

For a data set with n observations, the relative frequency of each bin can be determined as follows

Frequency of Bin / number of observations

Question 15

Relative frequency distribution

Answer 15

A relative frequency distribution is a tabular (any summary that uses a table) summary of data showing the relative frequency for each bin

Question 16

percent frequency distribution

Answer 16

A percent frequency distribution summarizes the percent frequency of the data for each bin

Question 17

Bin Width formula

Answer 17

Largest data- smallest data / number of bins = BW (always round up)

Bin widths calculations:

(MIN,MIN+BW), (UB1,UB1+BW), (UB2,UB2+BW)

Question 18

what kind of data does Histogram use

Answer 18

a common graphical presentation of quantitative data

Question 19

What is a histogram

Answer 19

histogram is a column chart with no spaces between the columns whose heights represent the frequencies of the corresponding bins

Question 20

Frequency polygon

Answer 20

A frequency polygon is useful for comparing quantitative distributions.

-A frequency polygon uses lines to connect the frequency counts of observations from
different bins.

Question 21

Cumulative Frequency distributions

Answer 21

A cumulative frequency distribution is a variation of the frequency distribution that provides another tabular summary of quantitative data.

• It uses the number of classes, class widths, and
  class limits developed for the frequency
  distribution.
• Shows the number of data items with values less
  than or equal to the upper-class limit of each class.

Question 22

Skewness

Answer 22

Skewness, or lack of symmetry, is an important characteristic of the shape of a distribution

Skewed Left= data is higher at right and lower at left
Skewed right= data is higher at left and lower at right
symmetrical= data looks like a pyramid

Skewness can be highly or moderately depending on how skewed

Question 23

Mean

equation

Answer 23

The most common measure of central location is the mean, the average of all the data values.
The population mean is denoted by the Greek letter, 𝜇

Sum of All Data / # of data sets = mean (average)

Question 24

Mode

Answer 24

The mode of a data set is the value that occurs with the greatest frequency (number that is seen the most)

The greatest frequency may occur at two or more different values. In these instances, more
than one mode exists.

Question 25

Bimodal

Answer 25

Data set has exactly 2 modes

Question 26

Multimodal

Answer 26

Data has more than 2 modes

Question 27

Median

Answer 27

The median is the value in the middle of a data set when data are arranged in ascending order. a) if n is odd, the median is the middle value b) if n is even, the median is the average of the two middle values

Question 28

What would a perfectly Symmetrical distribution look like?

Answer 28

Histogram would look like a pyramid, highest values in the middle the MEAN would be equal to the MEDIAN

Question 29

Geometric Mean | equation

Answer 29

The geometric mean is often used in analyzing growth rates in financial data (where using the arithmetic mean will provide misleading results) it should be applied any time you want to determine the mean rate of change over several successive periods (be it years, quarters, weeks, etc. equation= Nroot of all data multiplied together Nroot= number of data set root

Question 30

Range | equation

Answer 30

Simplest form of variability Range = Largest value- smallest value

Question 31

Variance and Computation of variance | equation

Answer 31

-The variance is a measure of variability that utilizes all the data. -In most statistical applications, when we compute a sample variance, we are often interested in using it to estimate the unknown population variance word equation= sum of every data set- the mean, squared, then divided by number of samples -1 =(data 1- mean)^2 + (data2 - mean)^2.... =add all together = divded by number of samples (2) -1

Question 32

Standard Deviation

Answer 32

Square root of the variance= standard deviation a measure of how dispersed the data is in relation to the mean. Low, or small, standard deviation indicates data are clustered tightly around the mean, and high, or large, standard deviation indicates data are more spread out.

Question 33

Coefficient of Variation | equation

Answer 33

The coefficient of variation, usually expressed as a percentage, measures how large the standard deviation is relative to the mean. = standard deviation /mean

Question 34

Percentiles

Answer 34

Finding a specific percentage of a data set. exm: if you are looking for the 95% of data, that means that only 5% of data is higher than you To calculate the 𝑝th percentile of a data set, we must first sort the data in ascending order.

Question 35

Percentiles/Quartiles equation

Answer 35

lets find the 85th percentile and sample size is 12 L85= P/100 (n+1) L85=85/100 (12+1) L85=.85 x 13 L85= 11.05 this means that the 85th percentile will be 5% of the way between values 11 and 12 if value 11 = 298,000 and value 12= 456,250 85th percentile = 298,000 + 5% (456,250-298,000) = 305,912.50

Question 36

Quartiles

Answer 36

Quartiles are specific percentiles that divide the data set into four parts, with each part containing approximately 25% of the observations. Uses the same formula as Percentiles

Question 37

Interquartile range | equation

Answer 37

The difference between the third and first quartiles is often referred to as the interquartile range, or IQR Q3-Q1 = Interquartile range

Question 38

Boxplots

Answer 38

A boxplot, also known as box-and-whisker plot, is a graphical summary of the distribution of data developed from the quartiles for a data set.

Question 39

Outliers why should be taken into consideration

Answer 39

An outlier is an unusually small or unusually large value in a data set. Care should be taken when handling outliers, as they might be: - an incorrectly recorded data value

Question 40

Covariance

Answer 40

the directional relationship between the returns on 2 assets, similar to correlation but only gives direction

Question 41

Scatter chart

Answer 41

A scatter chart is a graph for analyzing the relationship between two variables. uses Linear lines to determine its correlation

Question 42

Correlation (coefficient)

Answer 42

One limitation of the covariance to describe the relationship between two variables is that its magnitude depends on the variables’ units of measurement. A standardized measure of linear association between two variables that takes on values between −1 and +1. Values near +1 indicate a strong negative, linear relationship, values near −1 indicate a strong positive linear relationship, and values, near zero indicate the lack of a linear relationship. gives strength, which will be a percentage which explains the direction

Question 43

Bins

Answer 43

The nonoverlapping groupings of data used to create a frequency distribution. Bins for categorical data are also known as classes