Summary
YSS-20306 - summary quantitative colleges
- Course
- Institution
Summary of the colleges of the quantitative part of the YSS-20306 box to the WUR, which is part of the second year of the BBC.
[Show more]
1 review
By: luukvanderhoorn • 5 year ago
Seller
Follow
BBCer2013
Reviews received
Content preview
Samenvatting QuantitativeLecture 1 – Introduction, refreshing, correlation and
simple regression analysis
Relation is possible in 3 ways:
- Positively related: + -> +
- Negatively related: + -> -
- Not related at all
Modelling relationships:
- Outcomei = (model) + errori
- X = predictor variable
- Outcomei = (b Xi) + errori
- If one X in the model -> b = Pearson product – moment correlation
coefficient, denoted by r
Covariance (p. 264)
- A measure of the ‘average’ relationship between two variables.
- The average cross-product deviation
- The variance represents: de gemiddelde waarde die de data afwijkt van de
mean (s2)
- When one variable deviates from its mean, we would expect the other
variable to deviate from its mean in a similar way
- Cross-product deviations: a measure of the total relationship between two
variables
- The average sum is known as the covariance
- Positive covariance: as one variable deviates from the mean, the other
variable deviates in the same direction
- Negative covariance: as one variable deviates from the mean (bijv.
Toeneemt), the other deviates from the mean in the opposite direction (->
afneemt)
- The covariance depends upon the scales of measurement used: it is not a
standardized measure
- Hierdoor kunnen we niet zeggen of een covariance groot of klein is.
Standardization (p. 266)
- The process of converting a variable into a standard unit of measurement
- Typically used: standard deviation unit
- It allows us to compare data when different units of measurement have
been used (vb. Kg en inch)
- The standard covariance = Pearson’s correlation coefficient = r
- Value between -1 and +1
- -1: perfecte negatieve correlatie
- +1: perfecte positieve correlatie
- Commonly used measure of an effect size (.1 = small, .3 = medium, .5 =
large)
- This is all about bivariate correlation (between 2 variables)
Significance of the correlation coefficient (p. 268)
- Test the hypothesis that the correlation is different from 0
- Testing it using z-score, but also with t-scores
1
, Betrouwbaarheidsintervallen voor r
- They tell us something about the likely value in the population
- Lower boundary: x – (1,96 x SE)
- Upper boundary: x + (1,96 x SE)
- Kan niet met spss?
You can’t say anything about causality (oorzaak-gevolg) (p. 270), because
of:
- The third variable problem: causality between two variables cannot be
assumed. There may be other measured or unmeasured variables affecting
the results
- Direction of causality: correlatie zegt niets over welke variabele invloed
heeft op de ander. Geen indicatie van de richting.
Sources of bias (p. 271)
- Linearity: if the relationship between variables is not linear, then this
model is invalid -> outcome needs to be interval scale
- Normality: we care about this only if we want confidence intervals or
significance tests and if the sample size is small -> p-p plot
Pearson’s r in SPSS (p. 275)
- R2 = coefficient of determination
- A measure of the amount of variability in one variable that is shared by the
other
Partial correlation (p. 281)
- A measure of the relationship between two variables while controlling the
effect of one or more additional variables on both
- With this we can address the third variable problem to some degree
Semi-partial correlation (p. 285)
- A measure of the relationship between two variables while controlling the
effect that one or more additional variables has on one of these variables
- The effect of the third variable on only one variable
Regression (p. 294)
- Outcomei = yi = (b0+b1 Xi) + Ei
- This equation keeps the fundamental idea that an outcome for a person
can be predicted from a model
- The form of the equation is a straight line ( y = ax + b )
- b0 = the intercept of the line
- b1 = the slope
- b0 + b1 Xi = predicted value
Estimating the model (p. 298)
- residual: The difference between what the model predicts and the
observed data
- Residual Sum of Squares = SSR: a measure of the variability that cannot
be explained by the model fitted to the data/ a gauge of how well a
particular line fits the data
- Ordinary Least Squares = OLS: a method of regression in which the
parameters of the model are estimated using the method of least squares
2
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller BBCer2013. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $5.88. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
56326 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy study notes for 14 years now