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hi everyone this is Phil from statistics.mentor.com.two short more of our capabilities were.looking here at another example of a.simple linear regression model where the.deepened variable is continuous what we.have here is crime data from comparable.areas of 12 cities and our variables is.number of offenses committed over period.and another column of number of.unemployed so in one area of a city the.number of unemployed was 2614 and the.crossbone number of offenses in that.area of that city was 6200 and each row.represents is the data for certain area.of of different city now.let's first load the data before I load.the data I look at the text file we note.that this file does not have a header.meaning something like this offence in.other words is unlabeled okay now I'm.going to use the read table command to.download the data and call that.thoughtcrime the data file because we.don't have a header here we do not have.a header can change this to false or we.can do away with that altogether.okay third press Enter.using the file choose command here this.is this pop-up box from which we can now.select our file crime open see it's.loaded with type of crime all right so.what we see here is the data twelve.cities and because there is no header.has given it just me you want v2 we want.to kind of make sense of v1 v2 so won't.change those two names we understand so.let's do that using the names command.names crime is that name of our data.they'll use the combined command and.let's call the first column that is.offense so let's say off the second one.is unemployment so let's call that.mmm then we type crying again.and now you can see real able to stuff.that we better understand because this.is the only data file that I'm gonna be.working with in this example it's easier.to use the attach command so that our.knows that all the commands telling it.to act on my data call crime at the end.of my analysis is good idea to detach.that's only right at the end okay well.we've stunned that kind of an out.regression analysis like this would like.to look at the correlation and the plot.so let's look a correlation use that.using the core command of correlation.and then the names of our variables.comma I never see the correlation is.positive not point 86 remember.correlation is on a scale from minus one.to plus one here point positive point.eight six indicates pretty high positive.correlation of positive linear.relationship between offense this and.unemployment and the scatterplot goes.well with this so offense have this.recall that if I use plot first argument.comma second argument the first argument.a along the vert along horizontal x-axis.so what you see here is that as the.offense offenses increase so does.unemployment now here we have to ask.ourselves in this scenario what is the.DV dependent variable as unemployment or.is offense so one of them's the deepened.variable the other one is the.independent explanatory variable so it.makes sense to say that offense is the.independent variable.no next I said offense is the deepened.variable then unemployment is the.independent or explanatory variable so.we've got our plot the wrong way around.so to get the right way around just.simply go up this edit it and then.Peugeot's is the my independent variable.that goes along a horizontal offense.goes on the vertical axis so there you.go so if we're looking at this it says.that unemployment we read it like this.as unemployment kind of increases.because he on a scale from 1000 on words.the tendency for offense is also to.increase imagine here the orga see each.dot represents the the the unemployment.no offense rate for that city so that's.a city there's a city a city so it's 12.dots corresponding to each of the twelve.cities.noka C is a positive correlation would.like to quantify that fitting here.regression so let's do that reg that's.called well first of all give it a name.name for object of the regression of.just call it reg you call anything you.like the command for the for estimation.here LM for linear model dependent.variable name of dependable comes next.that is offense tilde and then the name.of the independent or sponsor rival is.unemployment enter and then we want to.see look at the summary results from.this to get the standard output here you.are so what can we see some statistics.about the residuals here but the one we.want is the coefficients and also down.here now.this and this this is the estimate of.the intercept this figure here is the.estimate of the slope coefficient for an.employment now I'm going to make a very.big assumption that all the assumptions.of classical regression model are.satisfied and so these statistics I'm.gonna look at make sense the T statistic.here's the test for the individual.significance of each of these.coefficients for unemployment T.statistic is five point three four and.the p value is less than well it's very.tiny it is less than not point not one.so it's a highly significant now the T.test here this individual significance.the null hypothesis is that the.coefficient the true coefficient is zero.all right so since the p-value is low we.reject the null so in other words we'll.conclude that the coefficient on in Pond.is not zero and that means there is.evidence and here the star evidence of.positive linear relationship between.unemployment and and offense the.interpretation of this coefficient comes.next this reads if unemployment goes up.by additional by additional person then.the crime will go up by less than one is.not 0.75 instead of talking about.decimals we can kind of multiply things.up so we can say if an opponent said of.gone up by one goes up by 10 say then.the predicted number of offenses will go.up by about seven and a half oh you.could say that if an employment goes up.by.hundred then the model predicts that the.number of fences will increase by about.75 now usually in regression would.actually report this F statistic first.but in simple linear regression this F.statistic it's testing the same thing as.that T statistic which is why in any.coincidence of the p-value for this F is.identical to the p-value for the T on.the coefficient of the explanatory.variable the null hypothesis for this F.test is that the model has no.explanatory power another way of saying.that is that the coefficient on your.explanatory variables is zero since.there's only one going to a variable in.this model that is why the F the null of.the F is identical to the null of the T.need also it's no coincidence and if you.take this T Square what will you get.let's do it.five point three four four squared five.thousand five point three four four.squared twenty-eight point five five.eight three for that isn't it very.similar that's no question it's because.theoretically they're the same in a.simple linear regression model okay so.what does this line look like well I can.see that the intercept is four thousand.three hundred so for about sorry 4323 so.in other words the line cuts the y-axis.at that level and it's got a positive.slope so the line is upward sloping and.that is the slope so if we've got a B.Line and then the name of our our say.the object of the the name of the object.for the regression which I'll call reg.will see this so here it's cutting.around four thousand three hundred and.twenty three and the slope is positive.so in a perfect fit big horse the line.doesn't go for every single point but.then it can't because these lines are.not aligned in a line now we can get a.measure of how close the dots are in.general to the line I had kind of a.measure of fit by looking at the R.square all right.no the R square.we'll call the basic R square here it's.called the multiple R squared but for.simple linear regression this multiple.bit you can ignore a multiple bit so the.figure is 0.74 recall that the R square.is a number between 0 & 1 then if it's 1.it means that your luck in the cippolini.regression case it means your line.because all the dots in other words it's.a perfect fit it's perfect.so you can get a perfect prediction of Y.given X in other words and upon I.thought Thanksgiving unemployment within.your sound pointer way right well let's.look at the ANOVA as well okay.so the ultimate thing about this an or.table regression is to get the F value.here which is basically my F what they.call the F statistic appear same thing.but here it's actually showing you how.you derive you get this number and get.these numbers by using these first three.columns in the UH Nova table okay if you.look at the mean square of the.regression unemployment here this means.square of your regression divided by mu.square residual will give you this job F.and that is a formula always works right.let's just relate to this the quantities.of this Hanover table to the a square.we've got here point seven four okay.recall that the formula relating the R.square to the is analyze it through the.sum of squares the R square is equal to.the regression sum of squares ie the.school Squires explained by your model.and employment here divided by total sum.squares in other words the sum of these.two regression sum of squares add it to.the residual sum of squares so let's.first add that let's get the total sum.squares by adding two separate sum of.squares together six pain plus that's.the regression sum of squares otherwise.known as explained to sum of squares.plus the residual sum of squares could.might all sum of squares is that now R.square is equal to the explained sum of.squares here's one for two six eight.divided by the total sum of squares just.calculated to find a seven point seven.four never go back up here what about.this R square point seven four fantastic.finally just to round off I want to want.you to think about let's just see what.happens when we take the square root of.this a square figure no point seven four.oh seven kept taking the square root of.R square figure not 0.86 we've seen that.number earlier on in in this session.yeah so is yes oh look at this.yeah in other words and this is no.coincidence.that simple linear regression square.root of your coefficient of.determination I the square root of big R.squared is equal in magnitude to the.correlation that's a fact.that's four simple linear regression.model now recall it we take a square.root of a number the answers + on - here.it's just giving you a plus so do you.know if it's not a negative correlation.instead by looking at the coefficients.here well you know that the sign of the.correlation should match the sign on the.slope coefficient because the sign let's.look what he tells you where as an.upward sloping line in other words.that's a positive correlation or as a.negative ago tells you it's a downward.sloping line which is negative.correlation okay so I think a bit more.stats there than anything but that's an.example of simple linear regression.modeling I hope you've got bits and bats.out of that.

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Reg 5103 Form FAQs

Locate answers to questions about Reg 5103 Form . Get the most frequently topics and more.

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I have created a registration form in HTML. When someone fills it out, how do I get the filled out form sent to my email?

Are you assuming that the browser will send the email? That is not the way it is typically done. You include in your registration form a <input type=submit> and use PHP or whatever on the server to send the email. In PHP it is PHP: mail - Manual But if you are already on the server it seems illogical to send an email. Just register the user immediately.

How can I fill out Google's intern host matching form to optimize my chances of receiving a match?

I was selected for a summer internship 2016. I tried to be very open while filling the preference form: I choose many products as my favorite products and I said I'm open about the team I want to join. I even was very open in the location and start date to get host matching interviews (I negotiated the start date in the interview until both me and my host were happy.) You could ask your recruiter to review your form (there are very cool and could help you a lot since they have a bigger experience). Do a search on the potential team. Before the interviews, try to find smart question that you are Continue Reading

Can I Smog my car in another state?

Simply put, YES. It is registered in California. How can you 'drive mainly in another state'? This would require that you are out of California more than there which also means the car should be registered in that location unless your home or 'home base' is CA.

What states are smog exempt?

The only thing they are exempt from is the yearly paying of registration fees. The state still sends the agency the correct year sticker, but they didn't pay for them. All other road-worthy requirements and rules still apply.

Does Pahrump require smog?

Most “smog” tests are very local decisions. You might have more luck speaking to the Michigan legislature? Ask them why they believe that Michigan’s air quality is so pristine that they don’t need emission testing?

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