opening regression upload

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Deskripsi matakuliah

Mempelajari :

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Analisis regresi linear sederhana

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Analisis regresi linear berganda

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Asumsi-asumsi dalam regresi

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Estimasi koefisien dan persamaan regresi

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Inferensi dan interpretasi dalam regresi

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Analisis variansi pada regresi

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Pendekatan matriks dalam analisis regresi

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Jumlah kuadrat ekstra

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Analisis korelasi

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Regresi lain (regresi polinomial, regresi

dummy,regresi logistik, regresi PLS)

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Referensi

Neter, John. 1990. Applied Linear Statistical Models : Regression, Analysis of Variance, and Experimental Design . Irwin : Boston

Model linier terapan I dan II (terjemahan)

Sumantri, B. (1997). Model Linear Terapan, Buku I. Jurusan Statistika: FMIPA IPB

Sumantri, B. (1997). Model Linear Terapan, Buku II. Jurusan Statistika: FMIPA IPB

Myers, R.H. (1996). Classical and Modern Regression with

Applications. Boston : PWS-KENT Publishing Company

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Kontrak perkuliahan

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Why study statistics?

Make decision without complete informations

Understanding population, sample

Parameter, statistic

Descriptive and inferential statistics

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glossary

A population is the collection of all items of interest or under investigation

N represents the population size

A sample is an observed subset of the population

n represents the sample size

A parameter is a specific characteristic of a population

Mean, Variance, Standard Deviation, Proportion, etc.

A statistic is a specific characteristic of a sample

Mean, Variance, Standard Deviation, Proportion, etc.

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Population vs. Sample

a b c d ef gh i jk l m n o p q rs t u v w x y z

Population

Sample

b c g i n o r u y

Values calculated using population data are called

parameters

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Examples of Populations

Incomes of all families living in yogyakarta

All women with pregnancy problem.

Grade point averages of all the students in your

university

…

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Random sampling

Simple random sampling

is a procedure in which

each member of the population is chosen

strictly by chance,

each member of the population is equally

likely to be chosen, and

every possible sample of n objects is

equally likely to be chosen

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Descriptive and Inferential Statistics

Two branches of statistics:

Descriptive statistics

Collecting, summarizing, and processing data to transform data into information

Inferential statistics

Provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge and decision

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Descriptive Statistics

Collect data

e.g., Survey

Present data

e.g., Tables and graphs

Summarize data

e.g., Sample mean =

i

X

n

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Inferential Statistics

Estimation

e.g., Estimate the population mean weight using the sample mean weight

Hypothesis testing

e.g., Test the claim that the

population mean weight is 120 pounds

opening regression

Inference is the process of drawing conclusions or making decisions about a

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The Decision Making Process

Begin Here:

Identify the Problem

Data Information

Knowledge Decision

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Independent and Dependent

Variable

Example case:

A real estate agent wishes to examine the

relationship between the selling price of a house

($1000s) and its size(measured in square feets)

Dependent variable (Y) = house price

in $1000s

Independent variable (X) = house’size

Dependent variable : response variable

Independent variable : predictor variable

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Sample Data for House

Price Model

House Price in $1000s

(Y) Square feets (X)

245 1400

312 1600

279 1700

308 1875

199 1100

219 1550

405 2350

324 2450

319 1425

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Scatter plot

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Graphical Presentation

House price model: scatter plot and

regression line

s) f

(square 0.10977

98.24833 price

house   eet

Slope = 0.10977

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Bagaimana mendapatkan

persamaan garis regresi ?