excerpts as pdf
... are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course. These notes were written to take advantage of R version 1.5.0 or later. For pedagogic ...
... are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course. These notes were written to take advantage of R version 1.5.0 or later. For pedagogic ...
Methods for Describing Sets of Data
... The difference between a bar chart and a histogram is that a bar chart is used for qualitative data and a histogram is used for quantitative data. For a bar chart, the categories of the qualitative variable usually appear on the horizontal axis. The frequency or relative frequency for each category ...
... The difference between a bar chart and a histogram is that a bar chart is used for qualitative data and a histogram is used for quantitative data. For a bar chart, the categories of the qualitative variable usually appear on the horizontal axis. The frequency or relative frequency for each category ...
Chapter Two (Data Types)
... – “Not applicable” data value when collected – Different considerations between the time when the data was collected and when it is analyzed. – Human/hardware/software problems ...
... – “Not applicable” data value when collected – Different considerations between the time when the data was collected and when it is analyzed. – Human/hardware/software problems ...
Data
... Data can be non-normal in a number of ways, e.g., the distribution may not be bell shaped or may be heavier tailed than the normal distribution or may not be symmetric. Only the departure from symmetry can be easily corrected by transforming the data. If the distribution is positively skewed, then t ...
... Data can be non-normal in a number of ways, e.g., the distribution may not be bell shaped or may be heavier tailed than the normal distribution or may not be symmetric. Only the departure from symmetry can be easily corrected by transforming the data. If the distribution is positively skewed, then t ...
Methods for Describing Sets of Data
... The difference between a bar chart and a histogram is that a bar chart is used for qualitative data and a histogram is used for quantitative data. For a bar chart, the categories of the qualitative variable usually appear on the horizontal axis. The frequency or relative frequency for each category ...
... The difference between a bar chart and a histogram is that a bar chart is used for qualitative data and a histogram is used for quantitative data. For a bar chart, the categories of the qualitative variable usually appear on the horizontal axis. The frequency or relative frequency for each category ...
Chapter 2 Data PreprocessinData Preprocessing
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
Chapter 2 Data Preprocessing
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
A Little Stats Won't Hurt You
... – The mean of a data set is the arithmetic average, which means that we take their sum and divide it by the number of data points. – The median of a data set is the “middle value,” meaning that 50% of the data is below this value. Arithmetically, this means that we order the data points. If we have ...
... – The mean of a data set is the arithmetic average, which means that we take their sum and divide it by the number of data points. – The median of a data set is the “middle value,” meaning that 50% of the data is below this value. Arithmetically, this means that we order the data points. If we have ...
Descriptive Statistics
... partitioning the data into smaller subsets, computing the measure for each subset, and then merging the results in order to arrive at the measure’s value for the original (i.e. entire) data set. ...
... partitioning the data into smaller subsets, computing the measure for each subset, and then merging the results in order to arrive at the measure’s value for the original (i.e. entire) data set. ...
Chapter 2 Data Preprocessing
... A univariate graphical method Consists of a set of rectangles that reflect the counts or frequencies of the classes present in the given data ...
... A univariate graphical method Consists of a set of rectangles that reflect the counts or frequencies of the classes present in the given data ...
No Slide Title
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
2. Data preprocessing
... • Example: if the dataset contains an attribute ‘Color’ with only three distinct values {Red, Green, Blue} then three attributes may be constructed: ‘Red’, ‘Green’ and ‘Blue’ where only one of them equals 1 (based on the value of ‘Color’) and the other two 0. • Another example: use a set of rules, d ...
... • Example: if the dataset contains an attribute ‘Color’ with only three distinct values {Red, Green, Blue} then three attributes may be constructed: ‘Red’, ‘Green’ and ‘Blue’ where only one of them equals 1 (based on the value of ‘Color’) and the other two 0. • Another example: use a set of rules, d ...
Point Processing Data
... Major Tasks in Data Preprocessing • Data cleaning – Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies • Data integration – Integration of multiple databases, data cubes, or files • Data transformation – Normalization and aggregation • Data reduction ...
... Major Tasks in Data Preprocessing • Data cleaning – Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies • Data integration – Integration of multiple databases, data cubes, or files • Data transformation – Normalization and aggregation • Data reduction ...
Data Mining: Concepts and Techniques
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
Data Mining: Concepts and Techniques
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
Lecture 2 - School of Computer Science and Software Engineering
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
... Faulty data collection instruments Human or computer error at data entry Errors in data transmission ...
normally distributed data
... and B but there is a greater range of values for A than for B. Curve C has the same distribution as A but the most common measurement is 18 which is twice that of curve A. All of these distributions are normal. The normal distribution is one of the most important of all distributions because it desc ...
... and B but there is a greater range of values for A than for B. Curve C has the same distribution as A but the most common measurement is 18 which is twice that of curve A. All of these distributions are normal. The normal distribution is one of the most important of all distributions because it desc ...