.
Csv()
Read.csv()
Read_csv()
B & c
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GetOption("defaultPackages")
Install.packages(packagenames)
Library(packagename)
Require(packagename)
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Character
Number
Factor
List
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1st-moment business decision
Data type
Storage mode
Data frame
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Data frame
Factor
Integer
Categorical
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Probability distribution
Normal distribution
Z value
Frequency distribution
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Electronic design automation
Economic development administration
Exploratory data analysis
Electronic data access
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Pie
Pieplot
Pipe
Package
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1, 2
1, 3, 4
3, 4, 5
1, 2, 3
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1, 2
1, 3, 4
3, 4
1, 2, 3
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Glm()
Lm()
Linear()
Slm()
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1TB
2TB
5TB
8TB
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Lm()
Knn()
Glm()
Mean()
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Cor()
Plot()
Scatter()
Pairs()
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Sample variance
Population variance
Sample standard deviation
Population standard deviation
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2.015
2.575
1.960
1.645
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Regression
Classification
Clustering
Summarization
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Convert a list into vector
Convert a vector into list
Convert a categorical data into numeric
List of the variables defined
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0
1
2
3
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K-means clustering
K-medians clustering
K-modes clustering
K-medoids clustering
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1 and 2
1 and 3
2 and 3
1, 2 and 3
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Scree plot
Knn
Skewness
Dendrogram
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1, 2 and 3
1 and 3
1 and 2
All the above
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Continuous variables
Measuring variables
Flowchart variables
Discrete variables
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Classification and prediction
Dependency analysis
Apriori algorithms
Data description
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Estimate of cluster centroids
Tree showing relations between each other
Assignment of each point to clusters
All of the Mentioned
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Partitional
Hierarchical
Knn
PCA
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Office of Legislative Services
Ottawa Linux Symposium
Organizational Leadership Supervision
Ordinary Least Squares
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Tells about the height of the regression line
Tells about the slope of the line
Should be excluded if one variable has negative values
Is statistically significant if it is larger than 1.96
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Minimizing the sum of squared errors
Making the sum of squared errors equal to one
Minimizing the absolute difference of the residuals
Forcing the smallest distance between the actual and fitted values
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1% change in X is associated with a B1 % change in Y
Change in X by one unit is associated with a B1 change in Y
Change in X by one unit is associated with a 100 B1 % change in Y
1% change in X is associated with a change in Y of 0.01 B1
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Y = B0 + B1X + B2Y^2 + e
Y = B0 + B1Log(X) + e
Y = B0 + B1X + B2X^2 + e
Y^2 = B0 + B1X + e
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A specific value of the y-variable given a specific value of the x-variable
A specific value of the x-variable given a specific value of the y-variable
The strength of the relationship between the x and y variables
None of these
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Is also equal to 1
Lie between -1 or +1
Be either of -1 to +1
Must be -1
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Will have a positive slope
Will have a negative slope
Will have a positive y intercept
Could have either a positive or a negative slope
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The relationship between two categorical variables
The relationship between two quantitative variables
He relationship between a quantitative input variable and a categorical output variable
The relationship between a categorical input and a quantitative output variable
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It will always decrease correlation coefficient
It will always increase correlation coefficient
It might either decrease or increase a correlation coefficient, depending on its relation with other points
It will have no effect on the correlation coefficient
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Regression analysis
Discriminant analysis
Analysis of variance
Cluster analysis
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Euclidean distance
Ward.d2 distance
Chebychevâ€™s distance
Manhattan distance
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Clustering is used for analyzing data when the dependent variable is categorical and the independent variables are discrete
Clustering is also called classification analysis
Groups or clusters are suggested by the data
Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters
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