Linear regression - true/false - Numbas at mathcentre.ac.uk

Error

There was an error loading the page.

Contributors

Feedback

From users who are not members of Stats :

Norah Storey

said

Ready to use

4 years, 5 months ago

History

○

Norah Storey 4 years, 5 months ago

Gave some feedback: Ready to use

○

Mario Orsi 5 years, 2 months ago

Published this.

○

Mario Orsi 5 years, 2 months ago

Created this as a copy of Data basics - true/false.

Name	Status	Author	Last Modified
Bonding - true/false	Ready to use	Mario Orsi	02/05/2023 19:37
Inference for proportions - true/false	Ready to use	Mario Orsi	15/05/2023 11:12
Chi-square: true/false	Ready to use	Mario Orsi	08/12/2021 10:54
Paired data - true/false	Ready to use	Mario Orsi	31/01/2020 13:19
ANOVA - true/false	Ready to use	Mario Orsi	29/09/2021 09:29
Linear regression - true/false	Ready to use	Mario Orsi	28/04/2021 15:45
Multiple regression - true/false	Ready to use	Mario Orsi	17/05/2023 10:18
Confidence intervals - true/false	Ready to use	Mario Orsi	27/03/2020 16:48

There are 58 other versions that do you not have access to.

Question statement

Identify each of the following statements as true or false in relation to linear regression:

Give any introductory information the student needs.

			Name	Type	Generated Value

t_statements	list	List of 17 items
f_statements	list	List of 17 items
statements	list	List of 34 items
statement_marks	list	Nested 34×2 list
choices	list	[ 6, 30, 18, 32, 13, 11 ]

			Name	Type	Generated Value

y1	integer	6
y1hat	integer	5
e1	integer	1
e1false	integer	-1
y2	integer	5
e2	integer	5
y2hat	integer	0
y2hatfalse	integer	10
Rplus	number	0.72
Rminus	number	-0.97
R	number	0.55
R2	number	0.303
Rpc	number	55
R2pc	number	30

Automatically regenerate variables?

Name

Data type

Value

(text strings)

Description

Describe what this variable represents, and list any assumptions made about its value.

Can an exam override the value of this variable?

Generated value: `list`

[ "If the observed response is $\\var{y1}$ and the model prediction is $\\var{y1hat}$, the residual is $\\var{e1}$.", "If the observed response is $\\var{y2}$ and the residual is $\\var{e2}$, the model prediction is $\\var{y2hat}$.", "If a model underestimates an observation, the residual is positive.", "If a model overestimates an observation, the residual is negative.", "If a residual plot shows a random distribution around a horizontal line, it is reasonable to fit a linear model to the data.", "The correlation statistic $R$ quantifies the strength of the linear relationship between two variables. ", "For a strong and positive correlation, $R$ will be near +1.", "For a strong and negative correlation, $R$ will be near -1.", "If there is no apparent correlation, $R$ will be near 0.", "$R=\\var{Rminus}$ indicates a stronger linear relationship than $R=\\var{Rplus}$.", "$R^2$ quantifies the amount of variation in the response that is explained by the model.", "In a model predicting weight (in kg) from height (in cm) in adult males, $R$ is dimensionless (no units).", "In a model predicting weight (in kg) from height (in cm) in adult males, the intercept is in kg.", "In a model predicting weight (in kg) from height (in cm) in adult males, the slope is in kg/cm.", "The uncertainty associated with the slope estimate ($b_1$) is higher when there is a lot of scatter around the regression line.", "The correlation coefficient is always unitless.", "If $R=\\var{R}$, the amount of variation in the response that is explained by the model is $\\var{R2pc}\\%$ (rounded to the nearest percentage point)." ]

← Depends on:

This variable doesn't seem to be used anywhere.