STATISTICS IS THE STUDY OF DATA
... Do NOT group the data into intervals. Each data value is on its own line in the table. Procedure to estimate pth percentile using the cumulative relative frequency column. Look down the cumulative relative frequency table to look for the decismal value of p. ...
... Do NOT group the data into intervals. Each data value is on its own line in the table. Procedure to estimate pth percentile using the cumulative relative frequency column. Look down the cumulative relative frequency table to look for the decismal value of p. ...
Chapter 3 Combined
... *Because 30 falls above the maximum “usual” value, we can consider it to be a cookie with an unusually high number of chips. ...
... *Because 30 falls above the maximum “usual” value, we can consider it to be a cookie with an unusually high number of chips. ...
z-scores: Using Standard Deviation as a Ruler
... The trick in comparing very different-looking values is to use standard deviations as our rulers. The standard deviation tells us how the whole collection of values varies, so it’s a natural ruler for comparing an individual to a group. As the most common measure of variation, the standard deviation ...
... The trick in comparing very different-looking values is to use standard deviations as our rulers. The standard deviation tells us how the whole collection of values varies, so it’s a natural ruler for comparing an individual to a group. As the most common measure of variation, the standard deviation ...
STT 315
... class or level. For instance, the qualitative variable GENDER has two classes, namely Male and Female. • If we count number of observations belonging to each class, then this count is called class frequency or simply frequency. • Relative frequency of a class is obtained by dividing the class freque ...
... class or level. For instance, the qualitative variable GENDER has two classes, namely Male and Female. • If we count number of observations belonging to each class, then this count is called class frequency or simply frequency. • Relative frequency of a class is obtained by dividing the class freque ...
Lect 2
... Nominal attribute with only 2 states (0 and 1) Symmetric binary: both outcomes equally important e.g., gender Asymmetric binary: outcomes not equally important. e.g., medical test (positive vs. negative) Convention: assign 1 to most important outcome (e.g., HIV positive) Ordinal Valu ...
... Nominal attribute with only 2 states (0 and 1) Symmetric binary: both outcomes equally important e.g., gender Asymmetric binary: outcomes not equally important. e.g., medical test (positive vs. negative) Convention: assign 1 to most important outcome (e.g., HIV positive) Ordinal Valu ...
Measure of Dispersion - Tamalpais Union High School District
... Basic Concepts In statistics a population, includes all of the items of interest, and a sample, includes some of the items in the population. The study of statistics can be divided into two main areas. Descriptive statistics, has to do with collecting, organizing, summarizing, and presenting data ( ...
... Basic Concepts In statistics a population, includes all of the items of interest, and a sample, includes some of the items in the population. The study of statistics can be divided into two main areas. Descriptive statistics, has to do with collecting, organizing, summarizing, and presenting data ( ...
S1: Chapter 1 Data: Location
... The mean maths score of 20 pupils in class A is 62. The mean maths score of 30 pupils in class B is 75. a) What is the overall mean of all the pupils’ marks. b) The teacher realises they mismarked one student’s paper; he should have received 100 instead of 95. Explain the effect on the mean and medi ...
... The mean maths score of 20 pupils in class A is 62. The mean maths score of 30 pupils in class B is 75. a) What is the overall mean of all the pupils’ marks. b) The teacher realises they mismarked one student’s paper; he should have received 100 instead of 95. Explain the effect on the mean and medi ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).