0
What does it mean to make a prediction for data using a regression equation?
Wiki User
∙ 7y agoWant this question answered?
Be notified when an answer is posted
Add your answer:
Earn +20 pts
When using the Regression tools you cannot plot the points of your data and your model on the same graph?
False
What characteristic makes regression line of best fit?
The equation of the regression line is calculated so as to minimise the sum of the squares of the vertical distances between the observations and the line. The regression line represents the relationship between the variables if (and only if) that relationship is linear. The equation of this line ensures that the overall discrepancy between the actual observations and the predictions from the regression are minimised and, in that respect, the line is the best that can be fitted to the data set. Other criteria for measuring the overall discrepancy will result in different lines of best fit.
How do you forecast room occupancy of hotel?
You create a statistical model using data for any variable that you think might affect room occupancy. Where direct data are not available, you could use proxies. Using these data you could carry out a multiple regression which would give you the best variables to use and an equation, using those variables, to forecast occupancy.Some variables that may be of use:SeasonNumber of hotels/room in townYour prices, offersOther hotels' pricesYour quality (star) ratingCustomer satisfaction, your reputationLocation (your and others')Amenities (your and others')Special events - eg conferences, conventionsAdvertisingTies with airlines, car rental etc.You create a statistical model using data for any variable that you think might affect room occupancy. Where direct data are not available, you could use proxies. Using these data you could carry out a multiple regression which would give you the best variables to use and an equation, using those variables, to forecast occupancy.Some variables that may be of use:SeasonNumber of hotels/room in townYour prices, offersOther hotels' pricesYour quality (star) ratingCustomer satisfaction, your reputationLocation (your and others')Amenities (your and others')Special events - eg conferences, conventionsAdvertisingTies with airlines, car rental etc.You create a statistical model using data for any variable that you think might affect room occupancy. Where direct data are not available, you could use proxies. Using these data you could carry out a multiple regression which would give you the best variables to use and an equation, using those variables, to forecast occupancy.Some variables that may be of use:SeasonNumber of hotels/room in townYour prices, offersOther hotels' pricesYour quality (star) ratingCustomer satisfaction, your reputationLocation (your and others')Amenities (your and others')Special events - eg conferences, conventionsAdvertisingTies with airlines, car rental etc.You create a statistical model using data for any variable that you think might affect room occupancy. Where direct data are not available, you could use proxies. Using these data you could carry out a multiple regression which would give you the best variables to use and an equation, using those variables, to forecast occupancy.Some variables that may be of use:SeasonNumber of hotels/room in townYour prices, offersOther hotels' pricesYour quality (star) ratingCustomer satisfaction, your reputationLocation (your and others')Amenities (your and others')Special events - eg conferences, conventionsAdvertisingTies with airlines, car rental etc.
How can you find an equation for a trend line?
There are numerous ways to do this. I think the easiest is to put the data in excel and have excel show the trend line, equation, andcorrelation coefficient. Excel gives you several options to choose for the trend line analysis. The other way is if it is a linear relationship, you can do the linear regression analysis following the steps listed in the related link. If you are not familiar with regression analysis, it may not be easy for you to follow.
What is the statistical study of the relationship between two sets of data?
Regression.
How do you calculate regression equation?
what is the equation of the regression line for the given data(Age, Number of Accidents) (16, 6605), (17, 8932), (18, 8506), (19, 7349), (20, 6458), (21, 5974)
When comparing the 95 percent confidence and prediction intervals for a given regression analysis what is the relation between confidence and prediction interval?
Confidence interval considers the entire data series to fix the band width with mean and standard deviation considers the present data where as prediction interval is for independent value and for future values.
When using the Regression tools you cannot plot the points of your data and your model on the same graph?
False
In a regression of a time series that states data as a function of calendar year what requirement of regression is violated?
In a regression of a time series that states data as a function of calendar year, what requirement of regression is violated?
What means trying to tell what will happen in the future using current understanding or data?
A prediction.
What Is a Logistic Regression Algorithm?
Using real-world data from a data set, a statistical analysis method known as logistic regression predicts a binary outcome, such as yes or no. A logistic regression model forecasts a dependent data variable by examining the correlation between one or more existing independent variables. Please visit for more information 1stepgrow.
Points that represent data values are connected using line segments?
Not necessarily. In a scatter plot or regression they would not.
How does modeling differ from experimentation?
A prediction is somthing u guess .An experiment is somthing you do based off of a prediction
What is a prediction and how do we make prediction from data?
Dan Henderson vs. Rashad Evans Prediction
What characteristic makes regression line of best fit?
The equation of the regression line is calculated so as to minimise the sum of the squares of the vertical distances between the observations and the line. The regression line represents the relationship between the variables if (and only if) that relationship is linear. The equation of this line ensures that the overall discrepancy between the actual observations and the predictions from the regression are minimised and, in that respect, the line is the best that can be fitted to the data set. Other criteria for measuring the overall discrepancy will result in different lines of best fit.
Do you need more than 10 observations in linear regression?
This is a difficult question to answer. The pure answer is no. In reality, it depends on the level of randomness in the data. If you plot the data, it will give you an idea of the randomness. Even with 10 data points, 1 or 2 outliers can significantly change the regression equation. I am not aware of a rule of thumb on the minimum number of data points. Obviously, the more the better. Also, calculate the correlation coefficient. Be sure to follow the rules of regression. See the following website: http:/www.duke.edu/~rnau/testing.htm
If the regression sum of squares is large relative to the error sum of squares is the regression equation useful for making predictions?
If the regression sum of squares is the explained sum of squares. That is, the sum of squares generated by the regression line. Then you would want the regression sum of squares to be as big as possible since, then the regression line would explain the dispersion of the data well. Alternatively, use the R^2 ratio, which is the ratio of the explained sum of squares to the total sum of squares. (which ranges from 0 to 1) and hence a large number (0.9) would be preferred to (0.2).
