Standardizing Data and the Normal Distribution Part 2
Über den Vortrag
Der Vortrag „Standardizing Data and the Normal Distribution Part 2“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 1“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Scatterplots and Correlation
- Making a Scatterplot
- Choosing X and Y
- What is Correlation?
- Finishing the Calculation
- Correlation and Causation
Quiz zum Vortrag
What information can we gain from a scatterplot?
- We can determine the form of a relationship between two quantitative variables.
- We can determine the form of a relationship between two categorical variables.
- We can determine the correlation between two quantitative variables.
- We can determine the strength of a relationship between two categorical variables.
- We can determine the slippage between two quantitative variables.
If two quantitative variables are deemed to have a positive relationship, what does that mean?
- This means that as the values of the X variable increase, the values of the Y variable increase.
- This means that as the value of the X variable increases, the value of the Y variable decreases.
- This means that as the value of the X variable decreases, the value of the Y variable increases.
- This means that the values of X and Y are all positive.
- The values of X and Y are all positive or zero.
Which of the following is NOT true about correlation?
- Correlation measures the strength of a linear relationship between two categorical variables.
- Correlation measures the strength of a linear relationship between two quantitative variables.
- A correlation coefficient takes values between -1 and 1.
- Correlation indicates the direction of a linear relationship between two quantitative variables.
- A correlation coefficient may take on the value of 0.
Which of the following is a TRUE statement?
- Correlation does not have a unit of measurement.
- Two quantitative variables with correlation 0.6 have a stronger linear relationship than two quantitative variables with correlation -0.6.
- If two quantitative variables are highly correlated, it can be concluded that changing the value of the explanatory variable causes the change in the response variable.
- Outliers have little effect on the correlation.
- Two quantitative variables with correlation 0.6 have a stronger linear relationship than two quantitative variables with correlation -0.8.
Under which of the following situations is correlation an appropriate measure of the strength of the relationship between two variables?
- Correlation is appropriate when measuring the strength of a relationship between two quantitative variables that appear to be linearly related and have no outliers present.
- Correlation is appropriate when measuring the strength of the relationship between two categorical variables.
- Correlation is appropriate when measuring the strength of a relationship between two quantitative variables that appear to be linearly related and have several outliers present.
- Correlation is appropriate for measuring the strength of the relationship between two quantitative variables when the relationship appears nonlinear.
- Correlation is appropriate when measuring the strength of a relationship between two quantitative variables that appear to have a logarithmic relationship.
Which of the following numbers is likely to represent the correlation coefficient of two variables having a strong positive association?
- 0.89
- -1.3
- -0.89
- 0
- 1.5
Which of the following numbers is likely to represent the correlation coefficient of two variables having no association?
- 0
- -1.3
- -0.89
- 0.89
- 1.5
Which of the following numbers is likely to represent the correlation coefficient of two variables whose scatterplot is downward sloping?
- -0.89
- -1.3
- 0
- 0.89
- 1.5
Which of the following numbers is likely to represent the correlation coefficient of two variables whose scatterplot is upward sloping?
- 0.45
- -1.1
- -0.45
- 0
- 1.7
What is one characteristic to evaluate in a scatterplot?
- Direction
- Liquidity
- Height
- Width
- Z-axis
Dozent des Vortrages Standardizing Data and the Normal Distribution Part 2
David Spade, PhD
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.
Kundenrezensionen
5,0 von 5 Sternen
| 5 Sterne |
|
1 |
| 4 Sterne |
|
0 |
| 3 Sterne |
|
0 |
| 2 Sterne |
|
0 |
| 1 Stern |
|
0 |
I learned a lot with this lecture. Really, I like the way of explanation. I will recommend this lecture to everybody who is really wanted to study statistics.
