Quantitative Statistical Methods

Quantitative Statistical Methods

Required Readings: Petra Petrovics: SPSS Tutorial and Exercise Book Quantitative Information Forming Methods Time Series models of business prognostics Proposed Readings: Chris Brooks: Introductory Econometrics for Finance, Cambridge; Second Edition: Richard A. Defusco, CFA Dennis W. McLeavey, CFA Jerald E. Pinto, CFA David E. Runkley, CFA: Quantitative Investment Analysis, CFA Series; Second Edition:

Hungarian Proposed Readings: Sajtos Mitev: SPSS adatelemzési és kutatási kézikönyv Ketskeméthy Izsó: Bevezetés az SPSS programrendszerbe Naresh K. Malhotra: Marketingkutatás Budapest, 2005. Székelyi Mária-Barna Ildikó: Túlélőkészlet az SPSShez, Budapest, 2005.

Requirements Writen exam and computer exam with the help of SPSS

Introduction to Statistics Petra Petrovics

Statistics Statistics: is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. Practical activity to analyze data Set of data as a result of statistical activity Method Analyzing data Drawing conclusion

Data Gathering Trends and reports overview Observations Interview Focus group Survey Photo interview

Statistics Descriptive Statistics Study of how data can be summarized effectively to describe the important aspects of large data sets It turns data into information Data collection & analyzation Statistical Inference It is used when tentative conclusions about a population are drawn on the basis of a sample

Statistical Population All members of a specified group (N) It is a set of entities concerning which statistical inferences are to be drawn, often based on a random sample taken from the population. Discrete population Continuous population (interval)

Statistical Variables = Characteristic of a unit. (1) (2) Quantitative Qualitative Temporal Geographical Common Differential

Quantitative vs. Qualitative Quantitative data measures either how much or how many of something, i.e. a set of observations where any single observation is a number that represents an amount or a count. Qualitative data provide labels, or names, for categories of like items, i.e. a set of observations where any single observation is a word or code that represents a class or category. ~ categorical variable

Types of Quantitative Variables Continuous variables are those variables that have theoretically an infinite number of gradations between two measurements. For example, body weight of individuals, milk yield of cows or buffaloes etc. Most of the variables in biology are of continuous type. Discrete variables do not have continuous gradations but there is a definite gap between two measurements, i.e. they can not be measured in fractions. For example, number of eggs laid by hens, number of children in a family etc.

Scales of measurement from weakest to strongest - nominal scale - ordinal scale - interval scale - ratio scale

1. Nominal scale Numbers are labels of groups or classes Simple codes assigned to objects as labels For qualitative data, e.g. professional classification, geographic classification e.g. - blonde: 1, brown: 2, red: 3, black: 4 (a person with red hair does not possess more "hairness" than a person with blonde hair) - female: 1, male: 2

2. Ordinal scale Data elements may be ordered according to their relative size or quality, the numbers assigned to objects or events represent the rank order (1st, 2nd, 3rd etc.) e.g. top lists of companies

3. Interval scale Meaning of distances between any two observations The "zero point" is arbitrary Negative values can be used Ratios between numbers on the scale are not meaningful, so operations such as multiplication and division cannot be carried out directly e.g. temperature with the Celsius scale

4. Ratio scale Strongest scale of measurement Distances between observations and also the ratios of distances have a meaning Contains a meaningful zero e.g. mass, length, time a salary of $50,000 is twice as large as a salary of $25,000

SPSS (Statistical Package for the Social Sciences ) computer program used for statistical analysis 2 files: XY.sav - Data View XY.spo - Output It can be a longer name Short name; don t use space!! Number of the characters in the Data View Width of a column Just with upper case!!!

Exercise 1 Age Place of residence Scool year Grade (Statistics) T.E. 24 Miskolc 3 3 B.N. 32 Miskolc 2 4 H.L. 22 Felsőzsolca 3 2 K.O. 35 Miskolc 3 4 V.I. 27 Mályi 2 5 Source: XY Create a data set! Students at the University of Miskolc

Create a data set! Gazdaságtudományi Kar Exercise 2 Source: XY 2000 2004 2005 University student 47498 66309 70153 College student 54889 88384 109412 Number of the teachers 17302 19103 18098 Number of institutions 77 91 90 Number of students in the % of the 18-22-year-old population 10,4 13,9 15

Ordinal

Exercise 3 - Graphs Employee data.sav 1. Define simple boxplot of the current salary categorized by the employment category clustered by gender! Embellish the graph! Create a 2. column diagram 3. column diagram stacked by gender 4. pie chart of the employees grouped by the employment category! (in %) Embellish the graph!

1. 2. 3. 4.

5. Create a scatterplot of current salary and beginning salary if you set markers by gender! Graphs / Scatter/Dot / Simple Data Label Mode

Thanks for your attention! strolsz@uni-miskolc.hu