Math 140 Notes and Activity Packet (Word)
... Correlation : See if there is a linear relationship between two quantitative variables. The study of that relationship is often called “Correlation and Regression”. ...
... Correlation : See if there is a linear relationship between two quantitative variables. The study of that relationship is often called “Correlation and Regression”. ...
(Big) data analysis: Lecture 5, going into high dimensional datasets
... 1. We select a number of clusters that is larger than what we think we need 2. This is ok as long as clusters are not becoming too small. The guildeine is that we approximate a complex distribution using pieces we can understand. 3. An appropriate size of a cluster depends on the statistics we colle ...
... 1. We select a number of clusters that is larger than what we think we need 2. This is ok as long as clusters are not becoming too small. The guildeine is that we approximate a complex distribution using pieces we can understand. 3. An appropriate size of a cluster depends on the statistics we colle ...
Section 2.5 Measures of Position
... First quartile, Q1: About one quarter of the data fall on or below Q1. Second quartile, Q2: About one half of the data fall on or below Q2 (median). Third quartile, Q3: About three quarters of the data fall on or below Q3. ...
... First quartile, Q1: About one quarter of the data fall on or below Q1. Second quartile, Q2: About one half of the data fall on or below Q2 (median). Third quartile, Q3: About three quarters of the data fall on or below Q3. ...
3.4 Measures of Relative Standing
... Example: Scores on a test have a mean of 70 and a standard deviation of 11. Michelle has a score of 48. Convert Michelle's score to a z-score. Solution: Because the formulas for a sample and population are the same, it makes no difference which formula we use, however assuming we have all the scores ...
... Example: Scores on a test have a mean of 70 and a standard deviation of 11. Michelle has a score of 48. Convert Michelle's score to a z-score. Solution: Because the formulas for a sample and population are the same, it makes no difference which formula we use, however assuming we have all the scores ...
Correlations and Linear Regression
... Linear Regression Data Figure 3: A single outlier alters the slope of the line. The point may be erroneous but if not, a different test ...
... Linear Regression Data Figure 3: A single outlier alters the slope of the line. The point may be erroneous but if not, a different test ...
mean
... •Depict the nature of shape or shape of the data distribution •In a graph: Different graphs used for different types of data ...
... •Depict the nature of shape or shape of the data distribution •In a graph: Different graphs used for different types of data ...
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.).