# Standardizing Data and the Normal Distribution Part 1 von David Spade, PhD

### Über den Vortrag

Der Vortrag „Standardizing Data and the Normal Distribution Part 1“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 1“. Der Vortrag ist dabei in folgende Kapitel unterteilt:

• Standardizing Data and the Normal Distribution
• Shifting Data
• Rescaling Data
• The Normal Distribution
• The Empirical Rule
• The Z-Table
• Using the Z-table in reverse

### Quiz zum Vortrag

1. A shift of a data set means adding the same constant to each observation.
2. A shift of a data set means that each observation is multiplied by the same constant.
3. A shift of a data set means that each observation is squared.
4. A shift in a data set is the addition of a constant followed by the subtraction of a variable number.
5. A shift in a data set is the addition of a variable number.
1. We standardize data in order to ensure that all variables are on the same scale.
2. We standardize data in order to make the distribution more symmetric.
3. We standardize data in order to get rid of outliers.
4. Data standardization is common practice.
5. We standardize data in order to ensure that no variables are on the same scale.
1. The empirical rule tells us that for data that comes from a normal distribution, about 68% of the data lie within one standard deviation of the mean.
2. The empirical rule tells us that for any data set, about 68% of the data lie within one standard deviation of the median.
3. The empirical rule tells us that all data come from a normal distribution.
4. The empirical rule states that 95% of the data lie within three standard deviations.
5. The empirical rule states that 68% of the data lie within three standard deviations.
1. The z-table is used to find probabilities associated with the normal distribution by finding the z-score in the margins of the table and looking up the probability in the associated cell in the body of the table.
2. The z-table is used to find probabilities associated with any data set after the data values have been standardized.
3. The z-table is used to find probabilities associated with the normal distribution by finding the z -score in the body of the table and then looking for the probability in the margin.
4. The z-table is a way to standardize data.
5. The z-table is used to evaluate skewed data set by finding the z-score and looking at the probability in the margin.
1. A unimodal, symmetric histogram with a linear probability plot is roughly normal.
2. We can look at a histogram, and if the histogram is skewed or has multiple modes, we can conclude our data are normal
3. We can look at a normal probability plot, and if the plot shows a non-linear pattern, we can conclude our data are normal.
4. We can look at a normal probability plot, and if the plot shows random scatter, we can conclude our data are normal
5. A logarithmic probability plot is roughly normal.
1. -0.5
2. 0
3. 0.5
4. 8
5. 5
1. 60
2. 40
3. 50
4. 500
5. 5
1. 40
2. 50
3. 60
4. 500
5. 5
1. 5
2. 0
3. 15
4. 500
5. -5
1. 500
2. 5
3. 40
4. 50
5. 60

### Dozent des Vortrages Standardizing Data and the Normal Distribution Part 1

Dr. David Spade is an Assistant Professor of Mathematical Sciences and Statistics at the University of Wisconsin-Milwaukee and holds a courtesy appointment as an Assistant Professor of Statistics at the University of Missouri-Kansas City, USA.
He obtained his MS in Statistics in 2010 and then completed his PhD in Statistics from Ohio State University in 2013.
An experienced mathemathics instructor, Dr. Spade has been teaching diverse statistics courses from the introductory to the graduate level since 2007.
Within Lecturio, he teaches courses on Statistics.

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