1.2 Data Classification
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The document discusses different types and levels of data. There are two types of data: qualitative and quantitative. Qualitative data consists of attributes like names while quantitative data consists of numerical values. There are four levels of measurement for data: nominal, ordinal, interval, and ratio. Each level allows for different statistical calculations and comparisons of the data.
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References:
Agarwal, B. L. 2006. Basic Statistics. Revised Fourth
Edition. New Age International (P) Ltd.: New Delhi.
Broto, Antonio S. 2006. Statistics Made Simple.
National Book Store: Mandaluyong City.
Microsoft® Encarta® 2009. © 1993-2008 Microsoft
Corporation. All rights reserved.
Pennings, Paul, Keman, Hans, and Kleinnijenhuis, Jan
1999. Doing Research in Political Science: An
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This document discusses different types of data and scales of measurement. It explains that there are two types of data: primary data collected directly from sources and secondary data gathered from existing resources. There are four scales of measurement: nominal data uses numbers for categorization only; ordinal data ranks objects numerically; interval data has equal distances between categories; and ratio data includes a true zero point and meaningful ratios. Examples are provided to illustrate the differences between each scale of measurement.
Data measurement tools
This document discusses different types of data and scales of measurement. It explains that there are two types of data: primary data collected directly from sources and secondary data gathered from existing resources. There are four scales of measurement: nominal data uses numbers for categorization only; ordinal data ranks objects numerically; interval data has equal distances between categories; and ratio data includes a true zero point. Examples are provided to illustrate the differences between each scale.
Data analysis using spss
This document provides an overview of data analysis using SPSS. It discusses key concepts like variables, measurement scales, data types, statistical terminology, and the steps involved in data analysis using SPSS. The document defines nominal, ordinal, interval and ratio scales of measurement. It also describes the nature of data as categorical or metric, and the types of categorical and metric data. Furthermore, it outlines tasks like data preparation, coding, cleaning and the appropriate use of statistical tools for analysis in SPSS.
Scales.pptx
This document defines and provides examples of different types of variables and levels of measurement. There are two main types of variables: qualitative/attribute variables where the characteristic is non-numeric like gender or eye color; and quantitative variables where information is reported numerically like account balances or number of children. Quantitative variables can be discrete, taking on certain numeric values with gaps, or continuous, assuming any value within a range. There are also four levels of measurement: nominal for unordered categories; ordinal for ordered but not precisely differentiated categories; interval for ordered and precisely differentiated categories without a true zero; and ratio which has all interval properties plus a true zero point.
level of measurement TED TALK.docx
The document discusses the different levels of measurement: nominal, ordinal, interval, and ratio. Nominal measurement involves assigning numeric codes to categories but there is no inherent ordering. Ordinal measurement assigns numbers with a meaningful order or rank. Interval measurement implies that the distance between attributes has meaning. Ratio measurement produces order and difference between variables as well as a true zero value, allowing for more statistical analyses. Understanding the level of measurement is important for determining how to analyze and interpret data.
Spss guidelines
The document provides an overview of a training on using SPSS. It is divided into three parts:
1) Introduction to SPSS, including background, objectives, and definitions.
2) Dealing with SPSS, covering getting started, key terms, creating a code book, and data entry.
3) Data management and analysis using SPSS, including exploratory, descriptive, and inferential analysis.
The training invites participants to properly learn how to use SPSS and makes time for questions.
business Statistics project assingment
This document discusses different types of data and scales of measurement in statistics. It defines primary and secondary data, with primary data collected directly by researchers through surveys or experiments, and secondary data collected indirectly from existing sources. Variables are defined as things that provide data, such as age, height, or number of pets. The scales of measurement discussed are nominal, ordinal, interval, and ratio scales. Categorical and quantitative data are also defined, with categorical using nominal or ordinal scales and quantitative using interval or ratio scales. Finally, the document distinguishes between cross-sectional data collected at one time and time-series data collected over multiple time periods.
June 2015
This document provides an overview of using SPSS for statistical analysis. It discusses the SPSS interface, including the data view and variable view. It also covers how to enter data into SPSS, either by directly inputting it or importing an external file such as an Excel spreadsheet. The key aspects of entering data are setting up the variables through the variable view according to a code book, and then inputting the values into the rows of the data view. Saving files in SPSS format (.sav) and exporting to Excel are also reviewed.
How Rankings Superiorly Differ Than Ratings?
Despite ranking and rating being greatly considered similar and being used and referred interchangeably ranking and rating have a severe distinction amongst them. Generally, rating and ranking measurements criterion and both have had numerous advocacies of different theories, views and proposals. This paper solely serves the purpose of considering the differentiation and distinction between Rankings and Ratings and furthermore concludes how one is superior to the other. Critics have presented the points that ratings are a drawback as they tend to consequent as generally appointing all subjects high rankings, and one ratings entail the factor of multicollinearity or the feasibility of predicting one rating from the other with a considerable extent of correctness
1 3 Variables and Types of Data
This document discusses different types of variables and levels of measurement. It defines qualitative and quantitative variables, as well as discrete and continuous variables. There are four levels of measurement for variables: nominal, ordinal, interval, and ratio. Nominal variables classify without order, ordinal can be ranked but differences are unknown, interval are ranked with precise differences but no true zero, and ratio have a true zero and allow for ratios. The document provides examples for each variable type and level of measurement.
Editing, coding and tabulation of data
Its a fully detailed topic about Editing , Coding, Tabulation o Data in research work.
The editing , coding , tabulation of data is been explained in this ppt.
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Logarithmic functions are inverses of exponential functions. To graph a logarithmic function:
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This document discusses testing for homogeneity among populations using chi-square tests. It defines homogeneity as populations having the same structure or composition. A test of homogeneity determines if different populations have the same proportions for various categories. It requires using a contingency table and chi-square distribution. An example tests if the same proportion of males and females prefer different pet types using survey data from college students.
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The document provides an overview of the chi-square distribution and how it can be used for hypothesis testing. It discusses that the chi-square distribution is used to find critical values for determining the area under the curve for a given degrees of freedom. It also gives an example of how chi-square can be used to test if two variables such as keyboard type and time to learn typing are independent.
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This document discusses synthetic division and the remainder theorem. Synthetic division is a process that simplifies long division when dividing a polynomial by a linear factor of the form x - a. It involves setting up the coefficients of the polynomial and multiplying/adding through the process. The remainder theorem states that if a polynomial P(x) is divided by x - a, then the remainder is equal to P(a). It provides a quick way to find the remainder of a polynomial division problem by evaluating the polynomial at the value of a. Examples are given to demonstrate evaluating polynomials using the remainder theorem.
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Long division can be used to divide polynomials in a similar way to dividing numbers. The key steps are to set up the division problem, divide the term of the dividend by the term of the divisor, multiply the divisor by the quotient term and subtract, then bring down the next term of the dividend and repeat. This polynomial long division allows polynomials to be factored by finding all divisor polynomials that give a remainder of zero. The factor theorem can also be used to check if a linear polynomial is a factor by setting it equal to zero and checking if it makes the other polynomial equal to zero.
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This document discusses solving polynomial equations by factoring. It provides examples of factoring polynomials, including factoring the difference and sum of cubes. Factoring by substitution is also introduced as a method for factoring polynomials of degree 4 or higher. The document demonstrates solving polynomial equations by factoring the expressions and setting each factor equal to 0. Both real and imaginary solutions may be obtained depending on whether the factors are real or complex numbers. Graphing is presented as an alternative method to find real solutions of a polynomial equation.
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The document discusses inferences for correlation and regression. It provides an example of testing the correlation between percentage of population with a college degree (x) and percentage growth in income (y) for 6 Ohio communities. There is a positive correlation between x and y, but this does not necessarily mean higher education causes higher earnings. The document also discusses measuring the spread of data points around the least squares line, including the standard error of estimate, using an example of how much copper sulfate dissolves in water at different temperatures.
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This document provides guided notes on inferences for correlation and regression. It discusses how the sample correlation coefficient and least squares line estimate population parameters and require assumptions about the data. It also outlines how to test the population correlation coefficient using a significance test and interpret the results. An example is provided testing the correlation between education levels and income growth. Students are asked to practice computing the standard error of estimate from a data set and answering summary questions.
5.2
1. The document discusses writing polynomials in factored form and finding the zeros of polynomial functions. It defines linear factors, roots, zeros, and x-intercepts as equivalent terms.
2. Examples are provided of writing polynomials in factored form using the factor theorem to find the zeros, and then graphing the polynomial function based on its zeros.
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This document discusses how to describe the shape of a cubic function by listing it in standard form, describing the end behavior of the graph, determining the possible number of turning points using a table of values, and determining the increasing and decreasing intervals. It explains that to describe the shape, you identify the sign of the leading coefficient to determine the end behavior and the number of turning points, which is one less than the possible degree. The document also discusses using differences of consecutive y-values in a table to determine the least degree of the polynomial function that could generate the data, with constant first differences indicating linear, constant second differences indicating quadratic, and constant third differences indicating cubic.
5.1[1]
This document defines key concepts related to polynomials and polynomial functions. It defines monomials as terms involving variables and exponents, and polynomials as sums of monomials. The degree of a polynomial is the highest exponent among its terms. Polynomial functions are polynomials written in terms of a single variable. Standard form arranges polynomial terms by descending degree. Polynomials are classified by degree and number of terms. Higher degree polynomials can have more turning points and their end behavior depends on the leading term. Examples show determining standard form, classifying polynomials, identifying end behavior and increasing/decreasing parts of graphs.
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This document discusses scatter diagrams and linear correlation. It provides examples of scatter diagrams that do and do not show linear correlation. It defines the correlation coefficient r as a measure of linear correlation between two variables on a scatter plot, with values between -1 and 1. It presents formulas for calculating r and provides an example of computing r using wind velocity and sand drift rate data. It cautions that correlation does not necessarily imply causation and that lurking variables can influence the correlation between two variables.
9.2 lin reg coeff of det
1) Linear regression finds the "best-fitting" linear relationship between two variables by minimizing the vertical distances between the data points and the linear equation line.
2) The coefficient of determination, r^2, measures how well the linear relationship described by the regression line fits the actual data, with higher r^2 values indicating less unexplained variability.
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1.2 Data Classification
- 1. Section 1.2 Data Classification Larson/Farber 4th ed.
- 6. Solution: Classifying Data by Type Quantitative Data (Base prices of vehicles models are numerical entries) Larson/Farber 4th ed. Qualitative Data (Names of vehicle models are nonnumerical entries)
- 16. Summary of Four Levels of Measurement Larson/Farber 4th ed. Meaningful operations at the four levels of measurement: Level of Measurement Put data in categories Arrange data in order Subtract data values Determine if one data value is a multiple of another Nominal Yes No No No Ordinal Yes Yes No No Interval Yes Yes Yes No Ratio Yes Yes Yes Yes