Regresi.

Presentasi berjudul: "Regresi."— Transcript presentasi:

1 Regresi

2 Metode Regresi Analisis untuk menunjukkan hubungan/ketergantungan antara variabel terikat (dependent) dan tak terikat (independent) Menggunakan metode kwadrat terkecil

3 Assumptions of Regression
Data mengikuti distribusi normal. Varians dari variable terikat hendaknya sama untuk semua nilai variable tak terikat Hubungan antara variable terikat dan tak terikat hendaklah linier. Semua observasi hendaknya independent

4 Regresi Linier Sederhana
y = a + x y = variabel terikat sebagai hasil pemodelan atau peramalan a = perpotongan dengan sumbu tegak (intersep) atau konstanta regresi  = kemiringan (gradien) atau koefisien regresi x = variabel bebas

5 Pengujian Statistik Dalam Peramalan
Berdasarkan nilai koefisien korelasi (r)

6 Korelasi positif r = 0,4 dan r = 0,7

7 Korelasi positif r = 0,9 dan r = 1,0

8 Korelasi Negatif r = -0,4 & -0,7

9 Korelasi Negatif r = -0,90 & -1,0

10 Interpretasi Nilai Korelasi
Nilai Mutlak Koefisien Korelasi Intepretasi 0.90 – 1.00 Korelasi sangat tinggi 0.70 – 0.89 Korelasi tinggi 0.40 – 0.69 Korelasi sedang 0.20 – 0.39 Korelasi rendah 0.00 – 0.19 Korelasi sangat rendah

11 Diagnostic Procedures
Normal plot of residuals Histogram of residuals Residuals versus fits Residuals versus order

12 Diagnostic Procedures
Normal plot of residuals Data harus linier jika asumsi normalitas dipenuhi. Jika tidak, asumsi tidak terpenuhi.

13 Diagnostic Procedures
Histogram of residuals Gambar hendaknya membentuk bell-shaped dengan dengan mean = 0. Jika ada titik-titik di luar dari nol, maka mengindikasikan ada faktor2 yang mempengaruhi hasil

14 Diagnostic Procedures
Residuals versus fits. Plot hendaknya berbentuk acak (random) pada garis 0 Jika tidak, ada beberapa kesalahan Berikut adalah beberapa tanda2 kesalahan: Adanya titik2 yang bertambah/berkurang Adanya kecendrungan titik-titik data dominan di negatif atau positif Adanya kecendrungan bahwa titik-titik residual bertambah dengan tambahnya fits

15 Patterns for Residual Plots
Satisfactory Funnel Double bow Non-linear

16 VALIDITY In general, VALIDITY is an indication of how sound your research is. More specifically, validity applies to both the design and the methods of your research. Validity in data collection means that your findings truly represent the phenomenon you are claiming to measure. Valid claims are solid claims. Validity of an assessment is the degree to which it measures what it is supposed to measure. This is not the same as reliability, which is the extent to which a measurement gives results that are very consistent..

17 Validity Not all authors define sampling variability in the same way. According to Krippendorff (2012), sampling variability refers to how well a population is accurately represented by a sample. It can be measured by the following formula: N=the sample size n=population size

18 Content Validity Logical Rational Validity
When you create a test or questionnaire for a particular subject, you want the questions to actually measure what you want them to. For example, the AP Physics exam should cover all topics actually taught to students and not unrelated material like English or biology. This matching between test questions and the content the questions are supposed to measure is called content validity

19 Criteria Validity Criterion validity (or criterion-related validity) measures how well one measure predicts an outcome for another measure

20 Correlation is a large part of predictive validity.

21 What is Reliability? Reliability is a measure of the stability or consistency of test scores. You can also think of it as the ability for a test or research findings to be repeatable Internal reliability or internal consistency, is a measure of how well your test is actually measuring what you want it to measure. External reliability means that your test or measure can be generalized beyond what you’re using it for

22 The Reliability Coefficient
A reliability coefficient is a measure of how well a test measures achievement. It is the proportion of variance in observed scores (i.e. scores on the test) attributable to true scores (the theoretical “real” score that a person would get if a perfect test existed).

23 Reliability Coefficient
Cronbach’s alpha — the most widely used internal-consistency coefficient. A simple correlation between two scores from the same person is one of the simplest ways to estimate a reliability coefficient. If the scores are taken at different times, then this is one way to estimate test-retest reliability; Different forms of the test given on the same day can estimate parallel forms reliability. Pearson’s correlation can be used to estimate the theoretical reliability coefficient between parallel tests. The Spearman Brown formula is a measure of reliability for split-half tests. Cohen’s Kappa measures interrater reliability.

24 Cronbach’s Alpha Formula
Where: N = the number of items. c̄ = average covariance between item-pairs. v̄ = average variance.

25 Pearson Correlation