Mgmt Data Test

Data Definition

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

Scales of measurement: Nominal

labels or names used to identify an attribute

Scales of measurement: Ordinal

order or rank of the data is meaningful

Scales of measurement: Interval

the interval is expressed in terms of a fixed unit of measure

Scales of measurement: Ratio

(1) the ratio of two values is meaningful and must contain a zero value

The scale of measurement determines:

the amount of information and indicates the appropriate data summarization and statistical analyses

Statistical studies: observational

A survey is a good example. For example: Researchers observing a randomly selected group of customers that enter a Walmart Super center to collect data on variables such as time spent in the store, gender of the customer, and the amount spent

statistical study: experimental

Variable of interest is the Dependent Variable and one or more variables are identified and controlled to observe how dependent variable changes

Descriptive analysis

Analytical techniques that describe what happened in the past.

Predictive analysis

- Analytical techniques that use models constructed from past data to predict future.

- Helps assess the impact the impact of one variable on another

Prescriptive analysis

Analytical techniques that yield a best course of action to take

Frequency distribution

A frequency distribution is a tabular summary of data showing the number (frequency) of observations in each of several non-overlapping categories or classes.

Bar or Pie Chart

A is a graphical display for categorical data.

What can an excel pivot table be used for?

An Excel Pivot table can be used to create a Frequency Table and Associated Charts

when skewness is <0 means:

"tail to the left"

When skewness is >0 means:

"tail to the right"

what is the z score?

- The z-score is often called the standardized value. It denotes the number of standard deviations a data value is from the mean.

- Z<0 the means the observed value is less than the mean. Z>0 means it is above the mean and Z=0 then it has the same value as the mean.

For data having a bell-shaped distribution:

- Approximately 68% of the data values will be within +/− 1 standard deviation of its mean.

- Approximately 95% of the data values will be within +/− 2 standard deviations of its mean.

- Almost all (approximately 99.7%) of the data values will be within +/− 3 standard deviations of its mean.

What is an outlier?

An outlier is an unusually small or unusually large value in a data set.

- A data value with a z-score less than −3 or greater than +3 might be considered an outlier

-It might be:

• an incorrectly recorded data value

• a data value that was incorrectly included in the data set

• a correctly recorded unusual data value that belongs in the data set

What is the covariance?

The covariance is a measure of the linear association between two variables.

- Positive values indicate a positive relationship.

- Negative values indicate a negative relationship.

- Not a Measure of strength

What is correlation?

Correlation is a measure of linear association and strength.

- Varies from +1 to -1 with 0 (zero) meaning no relationship.

- Just because two variables are highly correlated, it does not mean that one variable is the cause of the other.

What is a poisson distributed random variable?

A Poisson distributed random variable is often useful in estimating the number of occurrences over a specified interval of time or space.

- Examples: Number of knotholes in X linear feet of pine board, Number of vehicles arriving at a toll booth in one hour, Number of leaks in 100 miles of pipeline, Bell Labs used the Poisson distribution to model the arrival of phone calls.

What are the family of continuous probability distributions are characterized by:

The probability of the random variable assuming a value within some given interval from x1 to x2 is defined to be the area under the graph of the probability density function between x1 and x2

Why do we select a sample for data?

The reason we select a sample is to collect data to answer a research question about a population.

- The sample results provide only estimates of the values of the population characteristics. The reason is simply that the sample contains only a portion of the population.

-With proper sampling methods, the sample results can provide "good" estimates of the population characteristics

- It is recommended that probability sampling methods (simple random, stratified, cluster, or systematic) be used. For these methods, formulas are available for evaluating the "goodness" of the sample results in terms of the closeness of the results to the population parameters being estimated

- An evaluation of the goodness cannot be made with non probability (convenience or judgment) sampling methods.

What is point estimation?

Point estimation is a form of statistical inference.

- In point estimation we use the data from the sample to compute a value of a sample statistic that serves as an estimate of a population parameter.

What are finite populations?

Finite populations are essentially known such as a membership roster, account numbers, andUPC codes.

- A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size n has the same probability of being selected.

- In large sampling projects, computer-generated random numbers are often used to automate the sample selection process.

For an infinite population:

- We must select a random sample in order to make valid statistical inferences about thepopulation from which the sample is taken.

- A random sample from an infinite population is a sample selected such that the following conditions are satisfied.

- Each element selected comes from the population of interest.

- Each element is selected independently