Access the equation menu. The regression equation will be put into Y1.
Access the vars menu and navigate to
<5: Statistics> .
,
Navigate to
<EQ> .
<1: RegEQ> contains the regression equation which will be entered in Y1.
Press the graphing mode button. The regression line will be superimposed over the scatter plot.
To see the residuals and use them to calculate the critical point for an outlier:
Access the list. RESID will be an item on the menu. Navigate to it.
,
[LIST] ,
<RESID>
Confirm twice to view the list of residuals. Use the arrows to select them.
,
The critical point for an outlier is:
where:
= number of pairs of data
= sum of the squared errors
Store the residuals in
[L3] .
,
,
[L3] ,
Calculate the
. Note that
,
[L3] ,
,
,
Store this value in
[L4] .
,
,
[L4] ,
Calculate the critical value using the equation above.
,
,
,
,
,
[V] ,
,
[LIST] ,
,
,
,
[L4] ,
,
,
Verify that the calculator displays: 7.642669563. This is the critical value.
Compare the absolute value of each residual value in
[L3] to 7.64. If the absolute value is greater than 7.64, then the (x, y) corresponding point is an outlier. In this case, none of the points is an outlier.
To obtain estimates of
y For various
x -values:
There are various ways to determine estimates for "
y. " One way is to substitute values for "
x " in the equation. Another way is to use the
on the graph of the regression line.
Ti-83, 83+, 84, 84+ instructions for distributions and tests
Distributions
Access
DISTR (for "Distributions").
For technical assistance, visit the Texas Instruments website at
(External Link) and enter your calculator model into the "search" box.
Binomial distribution
binompdf(
n ,
p ,
x ) corresponds to
P (
X =
x )
binomcdf(
n ,
p ,
x ) corresponds to
P (X ≤ x)
To see a list of all probabilities for
x : 0, 1, . . . ,
n , leave off the "
x " parameter.
Poisson distribution
poissonpdf(λ,
x ) corresponds to
P (
X =
x )
poissoncdf(λ,
x ) corresponds to
P (
X ≤
x )
Continuous distributions (general)
uses the value –1EE99 for left bound
uses the value 1EE99 for right bound
Normal distribution
normalpdf(
x ,
μ ,
σ ) yields a probability density function value (only useful to plot the normal curve, in which case "
x " is the variable)
normalcdf(left bound, right bound,
μ ,
σ ) corresponds to
P (left bound<
X <right bound)
normalcdf(left bound, right bound) corresponds to
P (left bound<
Z <right bound) – standard normal
invNorm(
p ,
μ ,
σ ) yields the critical value,
k :
P (
X <
k ) =
p
invNorm(
p ) yields the critical value,
k :
P (
Z <
k ) =
p for the standard normal
Student's
t -distribution
tpdf(
x ,
df ) yields the probability density function value (only useful to plot the student-
t curve, in which case "
x " is the variable)
tcdf(left bound, right bound,
df ) corresponds to
P (left bound<
t <right bound)
Chi-square distribution
Χ
2 pdf(
x ,
df ) yields the probability density function value (only useful to plot the chi
2 curve, in which case "
x " is the variable)
Χ
2 cdf(left bound, right bound,
df ) corresponds to
P (left bound<
Χ2 <right bound)
F distribution
Fpdf(
x ,
dfnum ,
dfdenom ) yields the probability density function value (only useful to plot the
F curve, in which case "
x " is the variable)
Fcdf(left bound,right bound,
dfnum ,
dfdenom ) corresponds to
P (left bound<
F <right bound)
Tests and confidence intervals
Access
STAT and
TESTS .
For the confidence intervals and hypothesis tests, you may enter the data into the appropriate lists and press
DATA to have the calculator find the sample means and standard deviations. Or, you may enter the sample means and sample standard deviations directly by pressing
STAT once in the appropriate tests.
Confidence intervals
ZInterval is the confidence interval for mean when σ is known.
TInterval is the confidence interval for mean when σ is unknown;
s estimates σ.
1-PropZInt is the confidence interval for proportion.
Note
The confidence levels should be given as percents (ex. enter "
95 " or "
.95 " for a 95% confidence level).
Hypothesis tests
Z-Test is the hypothesis test for single mean when σ is known.
T-Test is the hypothesis test for single mean when σ is unknown;
s estimates σ.
2-SampZTest is the hypothesis test for two independent means when both σ's are known.
2-SampTTest is the hypothesis test for two independent means when both σ's are unknown.
1-PropZTest is the hypothesis test for single proportion.
2-PropZTest is the hypothesis test for two proportions.
Χ
2 -Test is the hypothesis test for independence.
Χ
2 GOF-Test is the hypothesis test for goodness-of-fit (TI-84+ only).
LinRegTTEST is the hypothesis test for Linear Regression (TI-84+ only).
Note
Input the null hypothesis value in the row below "
Inpt ." For a test of a single mean, "
μ∅ " represents the null hypothesis. For a test of a single proportion, "
p∅ " represents the null hypothesis. Enter the alternate hypothesis on the bottom row.
Communication is effective because it allows individuals to share ideas, thoughts, and information with others.
effective communication can lead to improved outcomes in various settings, including personal relationships, business environments, and educational settings. By communicating effectively, individuals can negotiate effectively, solve problems collaboratively, and work towards common goals.
it starts up serve and return practice/assessments.it helps find voice talking therapy also assessments through relaxed conversation.
miss
Every time someone flushes a toilet in the apartment building, the person begins to jumb back automatically after hearing the flush, before the water temperature changes. Identify the types of learning, if it is classical conditioning identify the NS, UCS, CS and CR. If it is operant conditioning, identify the type of consequence positive reinforcement, negative reinforcement or punishment
nature is an hereditary factor while nurture is an environmental factor which constitute an individual personality. so if an individual's parent has a deviant behavior and was also brought up in an deviant environment, observation of the behavior and the inborn trait we make the individual deviant.
Samuel
I am taking this course because I am hoping that I could somehow learn more about my chosen field of interest and due to the fact that being a PsyD really ignites my passion as an individual the more I hope to learn about developing and literally explore the complexity of my critical thinking skills
hello. autism is a umbrella term. autistic kids have different disorder overlapping. for example. a kid may show symptoms of ADHD and also learning disabilities.
before treatment please make sure the kid doesn't have physical disabilities like hearing..vision..speech problem. sometimes these
Jharna
continue..
sometimes due to these physical problems..the diagnosis may be misdiagnosed.
treatment for autism.
well it depends on the severity.
since autistic kids have problems in communicating and adopting to the environment.. it's best to expose the child in situations where the child
Jharna
child interact with other kids under doc supervision.
play therapy.
speech therapy.
Engaging in different activities that activate most parts of the brain.. like drawing..painting. matching color board game.
string and beads game.
the more you interact with the child the more effective
Jharna
results you'll get..
please consult a therapist to know what suits best on your child.
and last as a parent. I know sometimes it's overwhelming to guide a special kid.
but trust the process and be strong and patient as a parent.
Jharna
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