Formula to find correlation in spss 16
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Thus we can say that this value can be trusted. So we’ve modified the exponent in the formula for x - the mean and incorporated y. Notice here in the formula that we’ve modified the orinigal variance formula and included our second variable y. Covariance is simply variance for two variables. And the correlation coefficient is also close to -1 representing a strong negative correlation. Since correlation is based on how two variables vary, naturally we are interested in covariance. Since the value of y is 202.48 which is not a an outlier. Since the number of workers is an x variable, we will replace x with 17 the regression equation and calculate the loss to the company. Now plug in the values in the equation below and calculate ”a”.Ĭ) Based on the regression line what will be the predicted loss from the company when there are 17 workers on duty?will you trust this value? Justify your answer. Use the formula that we discussed in linear regression chapter to calculate this line. Next we will plug in all the values in the formula of correlation coefficient that we studied above,ī) Calculate the least square regression line. The closer the value is to -1 or +1 the stronger the relationship is considered to be. It is considered a perfect negative correlation and if the correlation is +1 then it is considered a perfect positive correlation. E.g The price of shoes and jeans have nothing in common, thus if the price of jeans increases or falls it will have no effect on the price of shoes and so we can say that they have zero correlation with each other.The population correlation coefficient uses x and y as the population standard deviations and xy as the population covariance. Population Correlation Coefficient Formula. Where S x and S y are the sample standard deviations, and S xy is the sample covariance. Then in that case, both the variables X and Y are considered independent and are considered to have no linear dependency on each other. The formula is given by: r xy S xy /S x S y. As the price of one product increases its demand falls and as its price decreases its demand increases.E.g when you start exercising your weight reduces significantly.They are considered to have an inverse relationship
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When both the variables move in different direction.If income falls the expenditure also falls and vice versa. The expenditure of a family depends on their income.E.g sale of ice cream with change in temperature, if the temperature increases more ice cream is sold and as the temperature decreases less ice cream is sold.Is when both the variables have the same type of moment and they both rise or fall together in the same direction.It also helps us understand the strength of the relationship and whether the relationship between two variables is positive or negative. And its numerical value ranges from +1 to -1. The interdependence of the two variables is known as as rrelation is measured by coefficient of correlation which is denoted by ”r”. A good relation between the variables means that the line of best fit will pass through maximum points.r =+1 r = -1 perfect positive and negative correlation respectively.